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This document provides an introduction to machine learning applications using deep learning techniques. It discusses how deep learning can be applied to computer vision, text generation, reinforcement learning, and more. The document then explains key concepts in deep learning including neural networks, convolutional neural networks, pooling layers, dropout, and techniques for training neural networks like forward and backpropagation.

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Recurrent neural networks rnn

The document discusses recurrent neural networks (RNNs) and long short-term memory (LSTM) networks. It provides details on the architecture of RNNs including forward and back propagation. LSTMs are described as a type of RNN that can learn long-term dependencies using forget, input and output gates to control the cell state. Examples of applications for RNNs and LSTMs include language modeling, machine translation, speech recognition, and generating image descriptions.

ppt on machine learning to deep learning (1).pptx

The document provides an overview of machine learning, deep learning, and artificial intelligence. It begins with definitions of AI, machine learning, and deep learning. It then covers key topics like the levels of AI, types of AI, where AI is used, and why AI is booming. Sections are dedicated to machine learning, deep learning, the differences between AI, ML, and DL, and various machine learning and deep learning algorithms and applications.

Deep Learning: Application & Opportunity

This document provides an overview of deep learning, including its history, algorithms, tools, and applications. It begins with the history and evolution of deep learning techniques. It then discusses popular deep learning algorithms like convolutional neural networks, recurrent neural networks, autoencoders, and deep reinforcement learning. It also covers commonly used tools for deep learning and highlights applications in areas such as computer vision, natural language processing, and games. In the end, it discusses the future outlook and opportunities of deep learning.

Fuzzy Logic ppt

Fuzzy logic is a form of multivalued logic that allows intermediate values between conventional evaluations like true/false, yes/no, or 0/1. It provides a mathematical framework for representing uncertainty and imprecision in measurement and human cognition. The document discusses the history of fuzzy logic, key concepts like membership functions and linguistic variables, common fuzzy logic operations, and applications in fields like control systems, home appliances, and cameras. It also notes some drawbacks like difficulty in tuning membership functions and potential confusion with probability theory.

Convolution Neural Network (CNN)

The presentation is made on CNN's which is explained using the image classification problem, the presentation was prepared in perspective of understanding computer vision and its applications. I tried to explain the CNN in the most simple way possible as for my understanding. This presentation helps the beginners of CNN to have a brief idea about the architecture and different layers in the architecture of CNN with the example. Please do refer the references in the last slide for a better idea on working of CNN. In this presentation, I have also discussed the different types of CNN(not all) and the applications of Computer Vision.

Fuzzy logic

Fuzzy logic is a form of logic that deals with reasoning that is approximate rather than precise. It allows intermediate values to be defined between conventional evaluations like true/false, and uses a continuum of truth values between 0 and 1. Fuzzy logic is useful for problems with imprecise or uncertain data, and can represent human reasoning that uses approximate terms like "warm" or "fast". It has been applied in various systems to control variables like temperature, speed, and focus based on fuzzy linguistic rules.

Activation functions and Training Algorithms for Deep Neural network

Training of Deep neural network is difficult task. Deep neural network train with the help of training algorithms and activation function This is an overview of Activation Function and Training Algorithms used for Deep Neural Network. It underlines a brief comparative study of activation function and training algorithms.

Fuzzy+logic

This document provides an overview of fuzzy logic. It begins by defining fuzzy as not being clear or precise, unlike classical sets which have clear boundaries. It then explains fuzzy logic allows for partial set membership rather than binary membership. The document outlines fuzzy logic's ability to model imprecise or nonlinear systems using natural language-based rules. It details the key concepts of fuzzy logic including linguistic variables, membership functions, fuzzy set operations, fuzzy inference systems and the 5-step fuzzy inference process of fuzzifying inputs, applying fuzzy operations and implications, aggregating outputs and defuzzifying results.

Recurrent neural networks rnn

The document discusses recurrent neural networks (RNNs) and long short-term memory (LSTM) networks. It provides details on the architecture of RNNs including forward and back propagation. LSTMs are described as a type of RNN that can learn long-term dependencies using forget, input and output gates to control the cell state. Examples of applications for RNNs and LSTMs include language modeling, machine translation, speech recognition, and generating image descriptions.

ppt on machine learning to deep learning (1).pptx

The document provides an overview of machine learning, deep learning, and artificial intelligence. It begins with definitions of AI, machine learning, and deep learning. It then covers key topics like the levels of AI, types of AI, where AI is used, and why AI is booming. Sections are dedicated to machine learning, deep learning, the differences between AI, ML, and DL, and various machine learning and deep learning algorithms and applications.

Deep Learning: Application & Opportunity

This document provides an overview of deep learning, including its history, algorithms, tools, and applications. It begins with the history and evolution of deep learning techniques. It then discusses popular deep learning algorithms like convolutional neural networks, recurrent neural networks, autoencoders, and deep reinforcement learning. It also covers commonly used tools for deep learning and highlights applications in areas such as computer vision, natural language processing, and games. In the end, it discusses the future outlook and opportunities of deep learning.

Fuzzy Logic ppt

Fuzzy logic is a form of multivalued logic that allows intermediate values between conventional evaluations like true/false, yes/no, or 0/1. It provides a mathematical framework for representing uncertainty and imprecision in measurement and human cognition. The document discusses the history of fuzzy logic, key concepts like membership functions and linguistic variables, common fuzzy logic operations, and applications in fields like control systems, home appliances, and cameras. It also notes some drawbacks like difficulty in tuning membership functions and potential confusion with probability theory.

Convolution Neural Network (CNN)

The presentation is made on CNN's which is explained using the image classification problem, the presentation was prepared in perspective of understanding computer vision and its applications. I tried to explain the CNN in the most simple way possible as for my understanding. This presentation helps the beginners of CNN to have a brief idea about the architecture and different layers in the architecture of CNN with the example. Please do refer the references in the last slide for a better idea on working of CNN. In this presentation, I have also discussed the different types of CNN(not all) and the applications of Computer Vision.

Fuzzy logic

Fuzzy logic is a form of logic that deals with reasoning that is approximate rather than precise. It allows intermediate values to be defined between conventional evaluations like true/false, and uses a continuum of truth values between 0 and 1. Fuzzy logic is useful for problems with imprecise or uncertain data, and can represent human reasoning that uses approximate terms like "warm" or "fast". It has been applied in various systems to control variables like temperature, speed, and focus based on fuzzy linguistic rules.

Activation functions and Training Algorithms for Deep Neural network

Training of Deep neural network is difficult task. Deep neural network train with the help of training algorithms and activation function This is an overview of Activation Function and Training Algorithms used for Deep Neural Network. It underlines a brief comparative study of activation function and training algorithms.

Fuzzy+logic

This document provides an overview of fuzzy logic. It begins by defining fuzzy as not being clear or precise, unlike classical sets which have clear boundaries. It then explains fuzzy logic allows for partial set membership rather than binary membership. The document outlines fuzzy logic's ability to model imprecise or nonlinear systems using natural language-based rules. It details the key concepts of fuzzy logic including linguistic variables, membership functions, fuzzy set operations, fuzzy inference systems and the 5-step fuzzy inference process of fuzzifying inputs, applying fuzzy operations and implications, aggregating outputs and defuzzifying results.

Deep Learning Tutorial | Deep Learning Tutorial For Beginners | What Is Deep ...

The document discusses deep learning and neural networks. It begins by defining deep learning as a subfield of machine learning that is inspired by the structure and function of the brain. It then discusses how neural networks work, including how data is fed as input and passed through layers with weighted connections between neurons. The neurons perform operations like multiplying the weights and inputs, adding biases, and applying activation functions. The network is trained by comparing the predicted and actual outputs to calculate error and adjust the weights through backpropagation to reduce error. Deep learning platforms like TensorFlow, PyTorch, and Keras are also mentioned.

Benchmark comparison of Large Language Models

The document summarizes the results of a benchmark comparison that tested several large language models across different skillsets and domains. It shows that GPT-4 performed the best overall based on metrics like logical robustness, correctness, efficiency, factuality, and common sense. Tables display the scores each model received for different skillsets and how they compare between open-sourced, proprietary, and oracle models. The source is listed as an unreviewed preprint paper and related GitHub page under a Creative Commons license.

Introduction to Deep Learning

This document provides an introduction to deep learning, including key developments in neural networks from the discovery of the neuron model in 1899 to modern networks with over 100 million parameters. It summarizes influential deep learning models such as AlexNet from 2012, ZF Net and GoogLeNet from 2013-2015, which helped reduce error rates on the ImageNet challenge. Top AI scientists who have contributed significantly to deep learning research are also mentioned. Common activation functions, convolutional neural networks, and deconvolution are briefly explained with examples.

neural networks

The document provides an overview of artificial neural networks (ANNs). It discusses how ANNs are modeled after biological neural networks and neurons. The key concepts covered include the basic structure and functioning of artificial neurons, different types of learning in ANNs, commonly used network architectures, and applications of ANNs. Examples of applications discussed are classification, recognition, assessment, forecasting and prediction. The document also notes how ANNs are used across various fields including computer science, statistics, engineering, cognitive science, neurophysiology, physics and biology.

Deep Learning - Convolutional Neural Networks

This document provides an agenda for a presentation on deep learning, neural networks, convolutional neural networks, and interesting applications. The presentation will include introductions to deep learning and how it differs from traditional machine learning by learning feature representations from data. It will cover the history of neural networks and breakthroughs that enabled training of deeper models. Convolutional neural network architectures will be overviewed, including convolutional, pooling, and dense layers. Applications like recommendation systems, natural language processing, and computer vision will also be discussed. There will be a question and answer section.

Intro to LLMs

A non-technical overview of Large Language Models, exploring their potential, limitations, and customization for specific challenges. While this deck is tailored for an audience from the financial industry in mind, its content remains broadly applicable.
(Note: Discover a slightly updated version of this deck at slideshare.net/LoicMerckel/introduction-to-llms.)

1.Introduction.ppt

This document outlines the syllabus for an Advanced Artificial Intelligence course. The course objectives are to learn the differences between optimal and human-like reasoning, understand state space representation and complexity, learn methods for solving problems using AI, be introduced to machine learning concepts, and learn probabilistic reasoning techniques. The syllabus covers topics like search strategies, constraint satisfaction problems, games, knowledge representation, planning, and uncertainty. Recommended textbooks are also listed.

Learning Deep Learning

This document provides an overview of deep learning concepts including neural networks, supervised learning, perceptrons, logistic regression, feature transformation, feedforward neural networks, activation functions, loss functions, and gradient descent. It explains how neural networks can learn representations through hidden layers and how different activation functions, loss functions, and tasks relate. It also shows examples of calculating the gradient of the loss with respect to weights and biases for logistic regression.

Computer vision introduction

This document provides an overview of a course on computer vision called CSCI 455: Intro to Computer Vision. It acknowledges that many of the course slides were modified from other similar computer vision courses. The course will cover topics like image filtering, projective geometry, stereo vision, structure from motion, face detection, object recognition, and convolutional neural networks. It highlights current applications of computer vision like biometrics, mobile apps, self-driving cars, medical imaging, and more. The document discusses challenges in computer vision like viewpoint and illumination variations, occlusion, and local ambiguity. It emphasizes that perception is an inherently ambiguous problem that requires using prior knowledge about the world.

AI - Fuzzy Logic Systems

This presentation educates you about AI - Fuzzy Logic Systems and its Implementation, Why Fuzzy Logic?, Why Fuzzy Logic?, Membership Function, Example of a Fuzzy Logic System and its Algorithm.
For more topics stay tuned with Learnbay.

What Is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutori...

This Deep Learning Presentation will help you in understanding what is Deep learning, why do we need Deep learning, applications of Deep Learning along with a detailed explanation on Neural Networks and how these Neural Networks work. Deep learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. This Deep Learning tutorial is ideal for professionals with beginners to intermediate levels of experience. Now, let us dive deep into this topic and understand what Deep learning actually is.
Below topics are explained in this Deep Learning Presentation:
1. What is Deep Learning?
2. Why do we need Deep Learning?
3. Applications of Deep Learning
4. What is Neural Network?
5. Activation Functions
6. Working of Neural Network
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms.
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning

Hopfield Networks

The document discusses Hopfield networks, which are neural networks with fixed weights and adaptive activations. It describes two types - discrete and continuous Hopfield nets. Discrete Hopfield nets use binary activations that are updated asynchronously, allowing an energy function to be defined. They can serve as associative memory. Continuous Hopfield nets have real-valued activations and can solve optimization problems like the travelling salesman problem. The document provides details on the architecture, energy functions, algorithms, and applications of both network types.

Soft computing

Soft computing is an emerging approach to computing that aims to mimic human reasoning and learning in uncertain and imprecise environments. It includes neural networks, fuzzy logic, and genetic algorithms. The main goals of soft computing are to develop intelligent machines to solve real-world problems that are difficult to model mathematically, while exploiting tolerance for uncertainty like humans. Some applications of soft computing include consumer appliances, robotics, food preparation devices, and game playing. Soft computing is well-suited for problems not solvable by traditional computing due to its characteristics of tractability, low cost, and high machine intelligence.

Neural Networks: Multilayer Perceptron

This document provides an overview of multilayer perceptrons (MLPs) and the backpropagation algorithm. It defines MLPs as neural networks with multiple hidden layers that can solve nonlinear problems. The backpropagation algorithm is introduced as a method for training MLPs by propagating error signals backward from the output to inner layers. Key steps include calculating the error at each neuron, determining the gradient to update weights, and using this to minimize overall network error through iterative weight adjustment.

Fuzzy Logic Seminar with Implementation

The document provides an overview of fuzzy logic and fuzzy sets. It discusses how fuzzy logic can handle imprecise data unlike classical binary sets. Membership functions assign degrees of membership values between 0 and 1. Fuzzy logic systems use if-then rules and linguistic variables. An example shows how fuzzy logic is used to estimate project risk levels based on funding and staffing levels. Fuzzy logic has been applied in various domains due to its ability to model human reasoning.

Fuzzy control and its applications

Fuzzy logic is a form of logic that accounts for partial truth and vagueness. It is used in control systems and decision support systems. The document discusses the history of fuzzy logic and its applications in areas like automotive, robotics, manufacturing, medical, and more. Fuzzy logic controllers combine fuzzy linguistic variables and rules to automate tasks like speed control in vehicles and temperature control in air conditioners and washing machines.

Regularization in deep learning

Presentation in Vietnam Japan AI Community in 2019-05-26.
The presentation summarizes what I've learned about Regularization in Deep Learning.
Disclaimer: The presentation is given in a community event, so it wasn't thoroughly reviewed or revised.

Machine Learning and Real-World Applications

This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.

Optimizers

I have implemented various optimizers (gradient descent, momentum, adam, etc.) based on gradient descent using only numpy not deep learning framework like TensorFlow.

Sequence to Sequence Learning with Neural Networks

This document discusses sequence to sequence learning with neural networks. It summarizes a seminal paper that introduced a simple approach using LSTM neural networks to map sequences to sequences. The approach uses two LSTMs - an encoder LSTM to map the input sequence to a fixed-dimensional vector, and a decoder LSTM to map the vector back to the target sequence. The paper achieved state-of-the-art results on English to French machine translation, showing the potential of simple neural models for sequence learning tasks.

00463517b1e90c1e63000000

This document discusses parallelizing object detection in videos for many-core systems. It presents an object detection algorithm that includes frame differencing, background differencing, post-processing, and background updating. The algorithm is parallelized by vertically partitioning video frames across cores, with some pixel overlap between partitions to reduce communication overhead. The parallel implementation achieves a speedup of 37.2x on a 64-core Tilera system processing 18 full-HD frames per second. A performance prediction equation is also developed and shown to accurately model the real performance results.

Deep Learning Tutorial | Deep Learning Tutorial For Beginners | What Is Deep ...

The document discusses deep learning and neural networks. It begins by defining deep learning as a subfield of machine learning that is inspired by the structure and function of the brain. It then discusses how neural networks work, including how data is fed as input and passed through layers with weighted connections between neurons. The neurons perform operations like multiplying the weights and inputs, adding biases, and applying activation functions. The network is trained by comparing the predicted and actual outputs to calculate error and adjust the weights through backpropagation to reduce error. Deep learning platforms like TensorFlow, PyTorch, and Keras are also mentioned.

Benchmark comparison of Large Language Models

The document summarizes the results of a benchmark comparison that tested several large language models across different skillsets and domains. It shows that GPT-4 performed the best overall based on metrics like logical robustness, correctness, efficiency, factuality, and common sense. Tables display the scores each model received for different skillsets and how they compare between open-sourced, proprietary, and oracle models. The source is listed as an unreviewed preprint paper and related GitHub page under a Creative Commons license.

Introduction to Deep Learning

This document provides an introduction to deep learning, including key developments in neural networks from the discovery of the neuron model in 1899 to modern networks with over 100 million parameters. It summarizes influential deep learning models such as AlexNet from 2012, ZF Net and GoogLeNet from 2013-2015, which helped reduce error rates on the ImageNet challenge. Top AI scientists who have contributed significantly to deep learning research are also mentioned. Common activation functions, convolutional neural networks, and deconvolution are briefly explained with examples.

neural networks

The document provides an overview of artificial neural networks (ANNs). It discusses how ANNs are modeled after biological neural networks and neurons. The key concepts covered include the basic structure and functioning of artificial neurons, different types of learning in ANNs, commonly used network architectures, and applications of ANNs. Examples of applications discussed are classification, recognition, assessment, forecasting and prediction. The document also notes how ANNs are used across various fields including computer science, statistics, engineering, cognitive science, neurophysiology, physics and biology.

Deep Learning - Convolutional Neural Networks

This document provides an agenda for a presentation on deep learning, neural networks, convolutional neural networks, and interesting applications. The presentation will include introductions to deep learning and how it differs from traditional machine learning by learning feature representations from data. It will cover the history of neural networks and breakthroughs that enabled training of deeper models. Convolutional neural network architectures will be overviewed, including convolutional, pooling, and dense layers. Applications like recommendation systems, natural language processing, and computer vision will also be discussed. There will be a question and answer section.

Intro to LLMs

A non-technical overview of Large Language Models, exploring their potential, limitations, and customization for specific challenges. While this deck is tailored for an audience from the financial industry in mind, its content remains broadly applicable.
(Note: Discover a slightly updated version of this deck at slideshare.net/LoicMerckel/introduction-to-llms.)

1.Introduction.ppt

This document outlines the syllabus for an Advanced Artificial Intelligence course. The course objectives are to learn the differences between optimal and human-like reasoning, understand state space representation and complexity, learn methods for solving problems using AI, be introduced to machine learning concepts, and learn probabilistic reasoning techniques. The syllabus covers topics like search strategies, constraint satisfaction problems, games, knowledge representation, planning, and uncertainty. Recommended textbooks are also listed.

Learning Deep Learning

This document provides an overview of deep learning concepts including neural networks, supervised learning, perceptrons, logistic regression, feature transformation, feedforward neural networks, activation functions, loss functions, and gradient descent. It explains how neural networks can learn representations through hidden layers and how different activation functions, loss functions, and tasks relate. It also shows examples of calculating the gradient of the loss with respect to weights and biases for logistic regression.

Computer vision introduction

This document provides an overview of a course on computer vision called CSCI 455: Intro to Computer Vision. It acknowledges that many of the course slides were modified from other similar computer vision courses. The course will cover topics like image filtering, projective geometry, stereo vision, structure from motion, face detection, object recognition, and convolutional neural networks. It highlights current applications of computer vision like biometrics, mobile apps, self-driving cars, medical imaging, and more. The document discusses challenges in computer vision like viewpoint and illumination variations, occlusion, and local ambiguity. It emphasizes that perception is an inherently ambiguous problem that requires using prior knowledge about the world.

AI - Fuzzy Logic Systems

This presentation educates you about AI - Fuzzy Logic Systems and its Implementation, Why Fuzzy Logic?, Why Fuzzy Logic?, Membership Function, Example of a Fuzzy Logic System and its Algorithm.
For more topics stay tuned with Learnbay.

What Is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutori...

This Deep Learning Presentation will help you in understanding what is Deep learning, why do we need Deep learning, applications of Deep Learning along with a detailed explanation on Neural Networks and how these Neural Networks work. Deep learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. This Deep Learning tutorial is ideal for professionals with beginners to intermediate levels of experience. Now, let us dive deep into this topic and understand what Deep learning actually is.
Below topics are explained in this Deep Learning Presentation:
1. What is Deep Learning?
2. Why do we need Deep Learning?
3. Applications of Deep Learning
4. What is Neural Network?
5. Activation Functions
6. Working of Neural Network
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you’ll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms.
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning

Hopfield Networks

The document discusses Hopfield networks, which are neural networks with fixed weights and adaptive activations. It describes two types - discrete and continuous Hopfield nets. Discrete Hopfield nets use binary activations that are updated asynchronously, allowing an energy function to be defined. They can serve as associative memory. Continuous Hopfield nets have real-valued activations and can solve optimization problems like the travelling salesman problem. The document provides details on the architecture, energy functions, algorithms, and applications of both network types.

Soft computing

Soft computing is an emerging approach to computing that aims to mimic human reasoning and learning in uncertain and imprecise environments. It includes neural networks, fuzzy logic, and genetic algorithms. The main goals of soft computing are to develop intelligent machines to solve real-world problems that are difficult to model mathematically, while exploiting tolerance for uncertainty like humans. Some applications of soft computing include consumer appliances, robotics, food preparation devices, and game playing. Soft computing is well-suited for problems not solvable by traditional computing due to its characteristics of tractability, low cost, and high machine intelligence.

Neural Networks: Multilayer Perceptron

This document provides an overview of multilayer perceptrons (MLPs) and the backpropagation algorithm. It defines MLPs as neural networks with multiple hidden layers that can solve nonlinear problems. The backpropagation algorithm is introduced as a method for training MLPs by propagating error signals backward from the output to inner layers. Key steps include calculating the error at each neuron, determining the gradient to update weights, and using this to minimize overall network error through iterative weight adjustment.

Fuzzy Logic Seminar with Implementation

The document provides an overview of fuzzy logic and fuzzy sets. It discusses how fuzzy logic can handle imprecise data unlike classical binary sets. Membership functions assign degrees of membership values between 0 and 1. Fuzzy logic systems use if-then rules and linguistic variables. An example shows how fuzzy logic is used to estimate project risk levels based on funding and staffing levels. Fuzzy logic has been applied in various domains due to its ability to model human reasoning.

Fuzzy control and its applications

Fuzzy logic is a form of logic that accounts for partial truth and vagueness. It is used in control systems and decision support systems. The document discusses the history of fuzzy logic and its applications in areas like automotive, robotics, manufacturing, medical, and more. Fuzzy logic controllers combine fuzzy linguistic variables and rules to automate tasks like speed control in vehicles and temperature control in air conditioners and washing machines.

Regularization in deep learning

Presentation in Vietnam Japan AI Community in 2019-05-26.
The presentation summarizes what I've learned about Regularization in Deep Learning.
Disclaimer: The presentation is given in a community event, so it wasn't thoroughly reviewed or revised.

Machine Learning and Real-World Applications

This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.

Optimizers

I have implemented various optimizers (gradient descent, momentum, adam, etc.) based on gradient descent using only numpy not deep learning framework like TensorFlow.

Sequence to Sequence Learning with Neural Networks

This document discusses sequence to sequence learning with neural networks. It summarizes a seminal paper that introduced a simple approach using LSTM neural networks to map sequences to sequences. The approach uses two LSTMs - an encoder LSTM to map the input sequence to a fixed-dimensional vector, and a decoder LSTM to map the vector back to the target sequence. The paper achieved state-of-the-art results on English to French machine translation, showing the potential of simple neural models for sequence learning tasks.

Deep Learning Tutorial | Deep Learning Tutorial For Beginners | What Is Deep ...

Deep Learning Tutorial | Deep Learning Tutorial For Beginners | What Is Deep ...

Benchmark comparison of Large Language Models

Benchmark comparison of Large Language Models

Introduction to Deep Learning

Introduction to Deep Learning

neural networks

neural networks

Deep Learning - Convolutional Neural Networks

Deep Learning - Convolutional Neural Networks

Intro to LLMs

Intro to LLMs

1.Introduction.ppt

1.Introduction.ppt

Learning Deep Learning

Learning Deep Learning

Computer vision introduction

Computer vision introduction

AI - Fuzzy Logic Systems

AI - Fuzzy Logic Systems

What Is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutori...

What Is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutori...

Hopfield Networks

Hopfield Networks

Soft computing

Soft computing

Neural Networks: Multilayer Perceptron

Neural Networks: Multilayer Perceptron

Fuzzy Logic Seminar with Implementation

Fuzzy Logic Seminar with Implementation

Fuzzy control and its applications

Fuzzy control and its applications

Regularization in deep learning

Regularization in deep learning

Machine Learning and Real-World Applications

Machine Learning and Real-World Applications

Optimizers

Optimizers

Sequence to Sequence Learning with Neural Networks

Sequence to Sequence Learning with Neural Networks

00463517b1e90c1e63000000

This document discusses parallelizing object detection in videos for many-core systems. It presents an object detection algorithm that includes frame differencing, background differencing, post-processing, and background updating. The algorithm is parallelized by vertically partitioning video frames across cores, with some pixel overlap between partitions to reduce communication overhead. The parallel implementation achieves a speedup of 37.2x on a 64-core Tilera system processing 18 full-HD frames per second. A performance prediction equation is also developed and shown to accurately model the real performance results.

03 image transformations_i

The document discusses image processing techniques including image derivatives, integral images, convolution, morphology operations, and image pyramids.
It explains that image derivatives detect edges by capturing changes in pixel intensity, and provides an example calculation. Integral images allow fast computation of box filters by precomputing pixel sums. Convolution is used to calculate probabilities as the sliding overlap of distributions. Morphology operations like erosion and dilation modify images based on pixel neighborhoods. Image pyramids create multiple resolution layers that aid in object detection across scales.

BMVA summer school MATLAB programming tutorial

This document discusses improving the runtime performance of MATLAB code through vectorization. It provides an example of an inefficient MATLAB function that approximates cycles of a square wave using sine waves. To optimize this code, the document suggests manipulating arrays rather than individual array elements, which can be done by removing the nested for loops. Vectorizing the code to operate on entire arrays at once rather than elements sequentially would improve performance. Profiling the code using MATLAB's profiler tool can help identify bottlenecks to target for optimization.

Keras on tensorflow in R & Python

Keras with Tensorflow backend can be used for neural networks and deep learning in both R and Python. The document discusses using Keras to build neural networks from scratch on MNIST data, using pre-trained models like VGG16 for computer vision tasks, and fine-tuning pre-trained models on limited data. Examples are provided for image classification, feature extraction, and calculating image similarities.

Generative modeling with Convolutional Neural Networks

CNNs, Adversarial examples, Generative Adversarial Networks, Best Practices, State-of-the-art models. Examples in Keras and PyTorch.

Scala and Deep Learning

An introduction to Deep Learning (DL) concepts, starting with a simple yet complete neural network (no frameworks), followed by aspects of deep neural networks, such as back propagation, activation functions, CNNs, and the AUT theorem. Next, a quick introduction to TensorFlow and Tensorboard, and then some code samples with Scala and TensorFlow.

Image De-Noising Using Deep Neural Network

Deep neural network as a part of deep learning algorithm is a state-of-the-art approach to find higher level
representations of input data which has been introduced to many practical and challenging learning
problems successfully. The primary goal of deep learning is to use large data to help solving a given task
on machine learning. We propose an methodology for image de-noising project defined by this model and
conduct training a large image database to get the experimental output. The result shows the robustness
and efficient our our algorithm.

Image De-Noising Using Deep Neural Network

Deep neural network as a part of deep learning algorithm is a state-of-the-art approach to find higher level representations of input data which has been introduced to many practical and challenging learning problems successfully. The primary goal of deep learning is to use large data to help solving a given task on machine learning. We propose an methodology for image de-noising project defined by this model and conduct training a large image database to get the experimental output. The result shows the robustness and efficient our our algorithm.

IMAGE DE-NOISING USING DEEP NEURAL NETWORK

Deep neural network as a part of deep learning algorithm is a state-of-the-art approach to find higher level representations of input data which has been introduced to many practical and challenging learning problems successfully. The primary goal of deep learning is to use large data to help solving a given task
on machine learning. We propose an methodology for image de-noising project defined by this model and conduct training a large image database to get the experimental output. The result shows the robustness and efficient our our algorithm.

Multilayer Perceptron (DLAI D1L2 2017 UPC Deep Learning for Artificial Intell...

Multilayer Perceptron (DLAI D1L2 2017 UPC Deep Learning for Artificial Intell...Universitat Politècnica de Catalunya

https://telecombcn-dl.github.io/2017-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.Deep learning (2)

Deep learning is a subset of machine learning and artificial intelligence that uses multilayer neural networks to enable computers to learn from large amounts of data. Convolutional neural networks are commonly used for deep learning tasks involving images. Recurrent neural networks are used for sequential data like text or time series. Deep learning models can learn high-level features from data without relying on human-defined features. This allows them to achieve high performance in application areas such as computer vision, speech recognition, and natural language processing.

Log polar coordinates

This document outlines an assignment for a computer vision course. Students are asked to implement 4 vision algorithms: 2 using OpenCV and 2 using MATLAB. The algorithms are the log-polar transform, background subtraction, histogram equalization, and contrast stretching. Students must also answer 3 short questions about orthographic vs perspective projection, efficient filtering, and sensors beyond cameras for computer vision.

28 01-2021-05

The document describes a study that used deep learning and convolutional neural networks to develop an image-based detection model for classifying four types of nuts (hazelnut, walnut, pecan, forest nut) with 100% accuracy on test data. The model was developed using Python in Google Colab, utilizing a dataset of 1595 images. A VGG16 model pre-trained on ImageNet was used to extract features from the images. The model contains convolutional and max pooling layers for feature extraction, and fully connected layers for classification. Training, validation, and testing of the model was performed in Google Colab using a GPU, demonstrating the feasibility of deep learning for nut detection applications.

C++ and Deep Learning

This document provides an overview and introduction to deep learning concepts including linear regression, activation functions, gradient descent, backpropagation, hyperparameters, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and TensorFlow. It discusses clustering examples to illustrate neural networks, explores different activation functions and cost functions, and provides code examples of TensorFlow operations, constants, placeholders, and saving graphs.

chap4_ann (5).pptx

The document provides an overview of artificial neural networks (ANN) including:
- The basic structure and learning process of perceptrons and multi-layer neural networks.
- How gradient descent can be used to train multi-layer networks by backpropagating errors.
- Examples of non-linearly separable data that require multi-layer networks.
- Design considerations for ANNs like number of layers/nodes and initialization of weights.
- Recent developments in deep learning and challenges/solutions for training deep networks.

Multilayer Perceptron - Elisa Sayrol - UPC Barcelona 2018

https://telecombcn-dl.github.io/2018-dlai/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.

Accelerated Logistic Regression on GPU(s)

The document summarizes a course project on accelerating logistic regression training using GPUs. The project involved implementing logistic regression on GPUs using techniques like parallel reduction, tiled computations, shared memory and streams. This led to an overall speedup of 57x compared to a CPU implementation. Key aspects included implementing sigmoid, gradient computation and weight update kernels optimized for GPU parallelism and memory access patterns. Data transposition and interleaving CPU/GPU tasks using streams further improved performance.

Image Classification using Deep Learning

1) The document discusses using a convolutional neural network (CNN) model for image classification of cats and dogs.
2) The CNN model architecture includes convolution, ReLU, max pooling, flatten, and fully connected layers to extract features and classify images.
3) The model was trained on a dataset of cat and dog images from Kaggle and tested on sample images, accurately classifying them as cats or dogs.

Neural Networks - How do they work?

This presentation begins with explaining the basic algorithms of machine learning and using the same concepts, discusses in detail 2 supervised learning/deep learning algorithms - Artificial neural nets and Convolutional Neural Nets. The relationship between Artificial neural nets and basic machine learning algorithms such as logistic regression and soft max is also explored. For hands on the implementation of ANN's and CNN's on MNIST dataset is also explained.

Eye deep

Eye deep

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00463517b1e90c1e63000000

03 image transformations_i

03 image transformations_i

BMVA summer school MATLAB programming tutorial

BMVA summer school MATLAB programming tutorial

Keras on tensorflow in R & Python

Keras on tensorflow in R & Python

Generative modeling with Convolutional Neural Networks

Generative modeling with Convolutional Neural Networks

Scala and Deep Learning

Scala and Deep Learning

Image De-Noising Using Deep Neural Network

Image De-Noising Using Deep Neural Network

Image De-Noising Using Deep Neural Network

Image De-Noising Using Deep Neural Network

IMAGE DE-NOISING USING DEEP NEURAL NETWORK

IMAGE DE-NOISING USING DEEP NEURAL NETWORK

Multilayer Perceptron (DLAI D1L2 2017 UPC Deep Learning for Artificial Intell...

Multilayer Perceptron (DLAI D1L2 2017 UPC Deep Learning for Artificial Intell...

Deep learning (2)

Deep learning (2)

Log polar coordinates

Log polar coordinates

28 01-2021-05

28 01-2021-05

C++ and Deep Learning

C++ and Deep Learning

chap4_ann (5).pptx

chap4_ann (5).pptx

Multilayer Perceptron - Elisa Sayrol - UPC Barcelona 2018

Multilayer Perceptron - Elisa Sayrol - UPC Barcelona 2018

Accelerated Logistic Regression on GPU(s)

Accelerated Logistic Regression on GPU(s)

Image Classification using Deep Learning

Image Classification using Deep Learning

Neural Networks - How do they work?

Neural Networks - How do they work?

Add a backend and deploy!

The document provides instructions for setting up a chatroom application using Firebase. It tells readers to install the necessary packages if they don't already have them from a previous workshop. This includes creating a React app, installing Firebase, and replacing the source code folder. It also mentions installing the Firebase CLI tools. The document then discusses additional hosting options for deploying the application such as Heroku, Firebase, GitHub Pages, and Netlify. It concludes by thanking readers for their time.

Build a chatroom!

1. The document discusses building a chatroom web application using React and Firebase. It explains using the create-react-app command to initialize a React project called chatroom, then installing the Firebase package.
2. Details are provided about client-server architecture and the roles of frontend and backend development. The technologies that will be used are listed as React, HTML, and Firebase for the frontend and backend.
3. Instructions are given to run the React development server after creating the project and installing Firebase to begin building the chatroom application.

Intro to React

This document provides an introduction to React including what React is, what will be covered in the workshop, what tools and skills are needed, and how to get started with a basic React app. React is introduced as a front end JavaScript library for building user interfaces. The workshop will cover building a chatroom app using React and Firebase. Developers will need a text editor, Node.js, programming experience, and familiarity with JavaScript and HTML. The document demonstrates how to initialize a React project and covers key React concepts like JSX, components, props, states, and event handling.

Building Beautiful Flutter Apps

The document provides an overview of UX/UI design and the Material Design framework. It defines UX design as focusing on the overall user experience and UI design as the visual interface between the user and product. The UX design process typically involves understanding users, research, analysis, design, launch, and evaluation. Material Design is introduced as a design language developed by Google that uses principles of good design and innovation. It provides standardized components, colors, typography, elevation, shapes, and icons to make developing applications across platforms easier.

Git & GitHub WorkShop

Git is a version control system that allows developers to have multiple versions of codebases and collaborate across teams. GitHub is a website that hosts Git repositories remotely, like Netflix for code. The document then discusses configuring and using Git and GitHub, including creating repositories, committing changes, pushing to remote repositories, branching, merging, and resolving conflicts. It provides resources for learning more about version control and Git/GitHub workflows.

Information session

This document summarizes an information session about the Boston University Developer Student Club (BU DSC). The BU DSC is a student-run community that empowers students through technology to solve local problems. It introduces the BU DSC team and leaders. It also outlines upcoming BU DSC events and workshops on topics like Git, GitHub, Flutter and career development. Students are encouraged to join events and provide feedback on what else they want to learn through a short Google form.

Flutter introduction

Flutter is an open-source framework for building beautiful, natively compiled mobile applications for iOS and Android from a single codebase. It allows developers to build fast, productive apps with no compromises for designers due to its optimized UI framework and productivity during development even while the app is running. Flutter is growing rapidly in popularity among software engineers and is used by major brands to build apps with large user bases.

Add a backend and deploy!

Add a backend and deploy!

Build a chatroom!

Build a chatroom!

Intro to React

Intro to React

Building Beautiful Flutter Apps

Building Beautiful Flutter Apps

Git & GitHub WorkShop

Git & GitHub WorkShop

Information session

Information session

Flutter introduction

Flutter introduction

"$10 thousand per minute of downtime: architecture, queues, streaming and fin...

Direct losses from downtime in 1 minute = $5-$10 thousand dollars. Reputation is priceless.
As part of the talk, we will consider the architectural strategies necessary for the development of highly loaded fintech solutions. We will focus on using queues and streaming to efficiently work and manage large amounts of data in real-time and to minimize latency.
We will focus special attention on the architectural patterns used in the design of the fintech system, microservices and event-driven architecture, which ensure scalability, fault tolerance, and consistency of the entire system.

Mutation Testing for Task-Oriented Chatbots

Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.

Getting the Most Out of ScyllaDB Monitoring: ShareChat's Tips

ScyllaDB monitoring provides a lot of useful information. But sometimes it’s not easy to find the root of the problem if something is wrong or even estimate the remaining capacity by the load on the cluster. This talk shares our team's practical tips on: 1) How to find the root of the problem by metrics if ScyllaDB is slow 2) How to interpret the load and plan capacity for the future 3) Compaction strategies and how to choose the right one 4) Important metrics which aren’t available in the default monitoring setup.

"Choosing proper type of scaling", Olena Syrota

Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.

Principle of conventional tomography-Bibash Shahi ppt..pptx

before the computed tomography, it had been widely used.

Discover the Unseen: Tailored Recommendation of Unwatched Content

The session shares how JioCinema approaches ""watch discounting."" This capability ensures that if a user watched a certain amount of a show/movie, the platform no longer recommends that particular content to the user. Flawless operation of this feature promotes the discover of new content, improving the overall user experience.
JioCinema is an Indian over-the-top media streaming service owned by Viacom18.

AWS Certified Solutions Architect Associate (SAA-C03)

AWS Certified Solutions Architect Associate (SAA-C03)

"What does it really mean for your system to be available, or how to define w...

We will talk about system monitoring from a few different angles. We will start by covering the basics, then discuss SLOs, how to define them, and why understanding the business well is crucial for success in this exercise.

Introducing BoxLang : A new JVM language for productivity and modularity!

Just like life, our code must adapt to the ever changing world we live in. From one day coding for the web, to the next for our tablets or APIs or for running serverless applications. Multi-runtime development is the future of coding, the future is to be dynamic. Let us introduce you to BoxLang.
Dynamic. Modular. Productive.
BoxLang redefines development with its dynamic nature, empowering developers to craft expressive and functional code effortlessly. Its modular architecture prioritizes flexibility, allowing for seamless integration into existing ecosystems.
Interoperability at its Core
With 100% interoperability with Java, BoxLang seamlessly bridges the gap between traditional and modern development paradigms, unlocking new possibilities for innovation and collaboration.
Multi-Runtime
From the tiny 2m operating system binary to running on our pure Java web server, CommandBox, Jakarta EE, AWS Lambda, Microsoft Functions, Web Assembly, Android and more. BoxLang has been designed to enhance and adapt according to it's runnable runtime.
The Fusion of Modernity and Tradition
Experience the fusion of modern features inspired by CFML, Node, Ruby, Kotlin, Java, and Clojure, combined with the familiarity of Java bytecode compilation, making BoxLang a language of choice for forward-thinking developers.
Empowering Transition with Transpiler Support
Transitioning from CFML to BoxLang is seamless with our JIT transpiler, facilitating smooth migration and preserving existing code investments.
Unlocking Creativity with IDE Tools
Unleash your creativity with powerful IDE tools tailored for BoxLang, providing an intuitive development experience and streamlining your workflow. Join us as we embark on a journey to redefine JVM development. Welcome to the era of BoxLang.

Lee Barnes - Path to Becoming an Effective Test Automation Engineer.pdf

So… you want to become a Test Automation Engineer (or hire and develop one)? While there’s quite a bit of information available about important technical and tool skills to master, there’s not enough discussion around the path to becoming an effective Test Automation Engineer that knows how to add VALUE. In my experience this had led to a proliferation of engineers who are proficient with tools and building frameworks but have skill and knowledge gaps, especially in software testing, that reduce the value they deliver with test automation.
In this talk, Lee will share his lessons learned from over 30 years of working with, and mentoring, hundreds of Test Automation Engineers. Whether you’re looking to get started in test automation or just want to improve your trade, this talk will give you a solid foundation and roadmap for ensuring your test automation efforts continuously add value. This talk is equally valuable for both aspiring Test Automation Engineers and those managing them! All attendees will take away a set of key foundational knowledge and a high-level learning path for leveling up test automation skills and ensuring they add value to their organizations.

Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors

Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host

AppSec PNW: Android and iOS Application Security with MobSF

Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.

[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...

The typical problem in product engineering is not bad strategy, so much as “no strategy”. This leads to confusion, lack of motivation, and incoherent action. The next time you look for a strategy and find an empty space, instead of waiting for it to be filled, I will show you how to fill it in yourself. If you’re wrong, it forces a correction. If you’re right, it helps create focus. I’ll share how I’ve approached this in the past, both what works and lessons for what didn’t work so well.

Containers & AI - Beauty and the Beast!?!

As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Keywords: AI, Containeres, Kubernetes, Cloud Native
Event Link: https://meine.doag.org/events/cloudland/2024/agenda/#agendaId.4211

What is an RPA CoE? Session 1 – CoE Vision

In the first session, we will review the organization's vision and how this has an impact on the COE Structure.
Topics covered:
• The role of a steering committee
• How do the organization’s priorities determine CoE Structure?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems

Demystifying Knowledge Management through Storytelling

The Department of Veteran Affairs (VA) invited Taylor Paschal, Knowledge & Information Management Consultant at Enterprise Knowledge, to speak at a Knowledge Management Lunch and Learn hosted on June 12, 2024. All Office of Administration staff were invited to attend and received professional development credit for participating in the voluntary event.
The objectives of the Lunch and Learn presentation were to:
- Review what KM ‘is’ and ‘isn’t’
- Understand the value of KM and the benefits of engaging
- Define and reflect on your “what’s in it for me?”
- Share actionable ways you can participate in Knowledge - - Capture & Transfer

A Deep Dive into ScyllaDB's Architecture

This talk will cover ScyllaDB Architecture from the cluster-level view and zoom in on data distribution and internal node architecture. In the process, we will learn the secret sauce used to get ScyllaDB's high availability and superior performance. We will also touch on the upcoming changes to ScyllaDB architecture, moving to strongly consistent metadata and tablets.

Astute Business Solutions | Oracle Cloud Partner |

Your goto partner for Oracle Cloud, PeopleSoft, E-Business Suite, and Ellucian Banner. We are a firm specialized in managed services and consulting.

GlobalLogic Java Community Webinar #18 “How to Improve Web Application Perfor...

Під час доповіді відповімо на питання, навіщо потрібно підвищувати продуктивність аплікації і які є найефективніші способи для цього. А також поговоримо про те, що таке кеш, які його види бувають та, основне — як знайти performance bottleneck?
Відео та деталі заходу: https://bit.ly/45tILxj

"NATO Hackathon Winner: AI-Powered Drug Search", Taras Kloba

This is a session that details how PostgreSQL's features and Azure AI Services can be effectively used to significantly enhance the search functionality in any application.
In this session, we'll share insights on how we used PostgreSQL to facilitate precise searches across multiple fields in our mobile application. The techniques include using LIKE and ILIKE operators and integrating a trigram-based search to handle potential misspellings, thereby increasing the search accuracy.
We'll also discuss how the azure_ai extension on PostgreSQL databases in Azure and Azure AI Services were utilized to create vectors from user input, a feature beneficial when users wish to find specific items based on text prompts. While our application's case study involves a drug search, the techniques and principles shared in this session can be adapted to improve search functionality in a wide range of applications. Join us to learn how PostgreSQL and Azure AI can be harnessed to enhance your application's search capability.

"$10 thousand per minute of downtime: architecture, queues, streaming and fin...

"$10 thousand per minute of downtime: architecture, queues, streaming and fin...

Mutation Testing for Task-Oriented Chatbots

Mutation Testing for Task-Oriented Chatbots

Getting the Most Out of ScyllaDB Monitoring: ShareChat's Tips

Getting the Most Out of ScyllaDB Monitoring: ShareChat's Tips

"Choosing proper type of scaling", Olena Syrota

"Choosing proper type of scaling", Olena Syrota

Principle of conventional tomography-Bibash Shahi ppt..pptx

Principle of conventional tomography-Bibash Shahi ppt..pptx

Discover the Unseen: Tailored Recommendation of Unwatched Content

Discover the Unseen: Tailored Recommendation of Unwatched Content

AWS Certified Solutions Architect Associate (SAA-C03)

AWS Certified Solutions Architect Associate (SAA-C03)

"What does it really mean for your system to be available, or how to define w...

"What does it really mean for your system to be available, or how to define w...

Introducing BoxLang : A new JVM language for productivity and modularity!

Introducing BoxLang : A new JVM language for productivity and modularity!

Lee Barnes - Path to Becoming an Effective Test Automation Engineer.pdf

Lee Barnes - Path to Becoming an Effective Test Automation Engineer.pdf

Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors

Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors

AppSec PNW: Android and iOS Application Security with MobSF

AppSec PNW: Android and iOS Application Security with MobSF

[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...

[OReilly Superstream] Occupy the Space: A grassroots guide to engineering (an...

Containers & AI - Beauty and the Beast!?!

Containers & AI - Beauty and the Beast!?!

What is an RPA CoE? Session 1 – CoE Vision

What is an RPA CoE? Session 1 – CoE Vision

Demystifying Knowledge Management through Storytelling

Demystifying Knowledge Management through Storytelling

A Deep Dive into ScyllaDB's Architecture

A Deep Dive into ScyllaDB's Architecture

Astute Business Solutions | Oracle Cloud Partner |

Astute Business Solutions | Oracle Cloud Partner |

GlobalLogic Java Community Webinar #18 “How to Improve Web Application Perfor...

GlobalLogic Java Community Webinar #18 “How to Improve Web Application Perfor...

"NATO Hackathon Winner: AI-Powered Drug Search", Taras Kloba

"NATO Hackathon Winner: AI-Powered Drug Search", Taras Kloba

- 1. BUMIC + DSC React Series Darcy 03/09/2021 Introduction to Applied Machine Learning
- 2. Applications of Deep Learning 1. Cool things using deep learning a. Computer Vision i. Tesla recognizing items on a street b. Text generation i. OpenAI GPT3 can solve almost any language task in a few examples c. Reinforcement Learning i. Can play Atari games, Board games, Real Time Strategy games ii. Robotic control d. Many more... 2
- 4. We have some data D 4 D X Y
- 5. Make an assumption about D 5 D X Y
- 6. What is learning? 6 The approximation of some unknown function f based on some data D. D X Y How do we set the parameters? How do we know what assumptions to make?
- 7. Intro to Deep Learning
- 8. What is Deep Learning 8 Artiﬁcial Intelligence Machine Learning Deep Learning Deep learning is a subset of machine learning
- 9. What is Deep Learning 9 Deep learning learns from data using a class of functions known as Neural Networks A neural network maps an input to an output
- 10. Biological Neuron vs. Artiﬁcial Neuron Andrej Karpathy
- 11. What is a Neural Network?
- 12. Steps to Train a NN
- 13. Push example through the network to get a predicted output Forward propagation Input Number of Bedrooms Number of Bathrooms Square Feet Hidden 1 Hidden 2 Hidden 3 Output Price of House
- 14. Calculate difference between predicted output and actual data Compute the cost Output Price of House D X Y
- 15. Calculate difference between predicted output and actual data Compute the cost Output Price of House Where i is the ith training example and m is the number of training examples
- 16. Push back the derivative of the error and apply to each weight, such that next time it will result in a lower error Backward propagation - “Update” https://hmkcode.github.io/ai/backpropagation-step-by-step/
- 18. Image Data 18 ● Images are commonly represented in code as a 3D array of pixels. Here, we notice 3 represents RGB values ● In vanilla neural networks, we would simply ﬂatten this 3D array into a 3072 length vector. However, by doing this, we lose spatial correlation between pixels close to other pixels
- 19. Image Data 19 ● In 2012 a paper called AlexNet out competed state of the art image classiﬁcation models through the usage of kernels (also called ﬁlters)
- 20. Kernel 20 ● Kernel: a small matrix used for feature detection on an image ○ Also called a ﬁlter ● Usage ○ Superimpose the kernel over a section of an image ○ Do element-wise multiplication between the weights in the kernel and the values in the image ○ Record the sum of the multiplications
- 21. Example Convolution 21 Example: Multiply the 5x5 image by a 3x3 kernel with weights: 1 0 1 0 1 0 1 0 1 The output? Sum of weight times part of image to a single number.
- 22. Kernel example 22 6 3 2 4 3 1 3 5 5 0 1 0 1 2 1 0 1 0 * = 19 Section of an image Kernel 6*0 3*1 2*0 4*1 3*2 1*1 3*0 5*1 5*0 = sum
- 23. Kernel 3 3 1 4 6 5 3 5 2 Kernel example (cont.) 23 Section of an image This image section contains the same values as before, but they have been rearranged, resulting in a greater activation with this kernel 0 1 0 1 2 1 0 1 0 * = 23 Section of an image 3*0 3*1 1*0 4*1 6*2 5*1 3*0 5*1 2*0 = sum
- 24. Example Convolution 24 ● Note that the output is smaller than the input ● This can be prevented by using padding around the edges of the image.
- 25. Padding 25 ● Before padding: ○ 7x7 input, 3x3 ﬁlter creating a 5x5 sized output ● After padding: ○ 9x9 input, 3x3 ﬁlter creating a 7x7 sized output which maintains the same size as our input ● Edges and corners aren’t as accurate but in practice this works well enough 7 9
- 26. Stride 26 ● Here, the kernel is moving one pixel at a time (“stride” = 1) ● The kernel can move by more than one pixel at a time ● Size = (N - F) / Stride + 1
- 27. Stride 27 ● Increasing stride decreases the size of the output ● Here, stride = 2 ● (N - F) / Stride + 1 (7 - 3) / 2 + 1 = 3
- 28. Dimensionality Practice 28 ● What would be the output size of a 5x5x3 ﬁlter with a 32x32x3 image and a stride of 1? ● (N - F) / Stride + 1
- 29. Dimensionality Practice 29 ● (32 - 5) / 1 + 1 = 28 ● Now let’s say we had a stride of 2, ○ (32 - 5) / 2 + 1 = 14.5 ○ Fractional size means the ﬁlter hangs off the input ○ We wouldn’t use this stride value consequently
- 30. Intentionally Shrinking Output Size 30 ● Now, let’s say you want to shrink your outputs (which are inputs to the next layer) to reduce operations. ● You can do this by either increasing the stride ○ (N - F)/Stride + 1 ● Alternatively, you can use a pooling layer
- 31. Conv Layer Output 31 ● Use multiple kernels for multiple activation maps ● In this example, we have 6 activation maps each created through a different ﬁlter with its own set of weights and biases
- 33. Pooling Layers 33 ● Limitation of output of Convolutional Layers: ○ Record the precise position of features in the input ○ Small movements in the position of the feature in the input image will result in a different feature map ● Solution: Pooling Layers ○ Lower resolution version of input is created with large and important structure elements preserved ○ Reduces the computational cost by reducing the number of parameters to learn
- 34. Max Pooling 34 Input (4 x 4) Output (2 x 2) Extracts the sharpest features of an image, making it more general
- 35. Average Pooling 35 Input (4 x 4) Output (2 x 2) Takes average feature of an image, minimize overﬁtting
- 36. Dropout 1. First, what is overﬁtting? a. Overﬁtting is when the neural network corresponds too closely to the dataset, and cannot be generalized. This tends to happen when a model is excessively complex relative to the data b. Conversely, underﬁtting is when the network cannot capture the underlying trend of the dataset which may happen if your network is not complicated enough. 36
- 37. Dropout - How can we solve overﬁtting? 1. Training phase a. Each weight has a probability p that they will be multiplied by zero (dropped). This probability is often set to 0.5, which is considered to be close to optimal for a wide range of networks and tasks b. This has the effect of removing random connections between activations effectively creating a new network/outlook on the data per each train set 37
- 38. Dropout - How can we solve overﬁtting? 2. Post Train a. After training weights will be abnormally high as they were adjusted assuming only (1-p) percent of the weights would be summed together and used. b. To ﬁx this we normalize weights to lower the expectation of each weight. We do this by scaling each weight by 1/p c. “This makes sure that for each unit, the expected output from it under random dropout will be the same as the output during pretraining.” ~Dropout: A Simple Way to Prevent Neural Networks from Overﬁtting i. http://www.jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf 38
- 40. So what does our network look like? 40 ●