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Convolutional neural networks (CNNs) learn multi-level features and perform classification jointly and better than traditional approaches for image classification and segmentation problems. CNNs have four main components: convolution, nonlinearity, pooling, and fully connected layers. Convolution extracts features from the input image using filters. Nonlinearity introduces nonlinearity. Pooling reduces dimensionality while retaining important information. The fully connected layer uses high-level features for classification. CNNs are trained end-to-end using backpropagation to minimize output errors by updating weights.

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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.

Convolutional Neural Network Models - Deep Learning

Convolutional Neural Network Models - Deep Learning
Convolutional Neural Network
ILSVRC
AlexNet (2012)
ZFNet (2013)
VGGNet (2014)
GoogleNet 2014)
ResNet (2015)
Conclusion
Convolutional Neural Network (CNN) is a multi-layer neural network

Convolutional Neural Network and Its Applications

In machine learning, a convolutional neural network is a class of deep, feed-forward artificial neural networks that have successfully been applied fpr analyzing visual imagery.

Convolutional neural network

Convolutional neural network (CNN / ConvNet's) is a part of Computer Vision. Machine Learning Algorithm. Image Classification, Image Detection, Digit Recognition, and many more. https://technoelearn.com .

Convolutional Neural Network (CNN)

Convolutional Neural Network (CNN)
Steps in CNN
Convolution
Max Pooling
Flattening
Full Connection

Convolutional Neural Networks

The document provides an overview of convolutional neural networks (CNNs) and their layers. It begins with an introduction to CNNs, noting they are a type of neural network designed to process 2D inputs like images. It then discusses the typical CNN architecture of convolutional layers followed by pooling and fully connected layers. The document explains how CNNs work using a simple example of classifying handwritten X and O characters. It provides details on the different layer types, including convolutional layers which identify patterns using small filters, and pooling layers which downsample the inputs.

Image classification using CNN

classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects.we'll preprocess the images, then train a convolutional neural network on all the samples. The images need to be normalized and the labels need to be one-hot encoded.

Convolutional Neural Networks (CNN)

A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).

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.

Convolutional Neural Network Models - Deep Learning

Convolutional Neural Network Models - Deep Learning
Convolutional Neural Network
ILSVRC
AlexNet (2012)
ZFNet (2013)
VGGNet (2014)
GoogleNet 2014)
ResNet (2015)
Conclusion
Convolutional Neural Network (CNN) is a multi-layer neural network

Convolutional Neural Network and Its Applications

In machine learning, a convolutional neural network is a class of deep, feed-forward artificial neural networks that have successfully been applied fpr analyzing visual imagery.

Convolutional neural network

Convolutional neural network (CNN / ConvNet's) is a part of Computer Vision. Machine Learning Algorithm. Image Classification, Image Detection, Digit Recognition, and many more. https://technoelearn.com .

Convolutional Neural Network (CNN)

Convolutional Neural Network (CNN)
Steps in CNN
Convolution
Max Pooling
Flattening
Full Connection

Convolutional Neural Networks

The document provides an overview of convolutional neural networks (CNNs) and their layers. It begins with an introduction to CNNs, noting they are a type of neural network designed to process 2D inputs like images. It then discusses the typical CNN architecture of convolutional layers followed by pooling and fully connected layers. The document explains how CNNs work using a simple example of classifying handwritten X and O characters. It provides details on the different layer types, including convolutional layers which identify patterns using small filters, and pooling layers which downsample the inputs.

Image classification using CNN

classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects.we'll preprocess the images, then train a convolutional neural network on all the samples. The images need to be normalized and the labels need to be one-hot encoded.

Convolutional Neural Networks (CNN)

A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).

Convolutional Neural Network - CNN | How CNN Works | Deep Learning Course | S...

A Convolutional Neural Network (CNN) is a type of neural network that can process grid-like data like images. It works by applying filters to the input image to extract features at different levels of abstraction. The CNN takes the pixel values of an input image as the input layer. Hidden layers like the convolution layer, ReLU layer and pooling layer are applied to extract features from the image. The fully connected layer at the end identifies the object in the image based on the extracted features. CNNs use the convolution operation with small filter matrices that are convolved across the width and height of the input volume to compute feature maps.

Deep neural networks

Deep learning and neural networks are inspired by biological neurons. Artificial neural networks (ANN) can have multiple layers and learn through backpropagation. Deep neural networks with multiple hidden layers did not work well until recent developments in unsupervised pre-training of layers. Experiments on MNIST digit recognition and NORB object recognition datasets showed deep belief networks and deep Boltzmann machines outperform other models. Deep learning is now widely used for applications like computer vision, natural language processing, and information retrieval.

Deep Learning Explained

This document summarizes Melanie Swan's presentation on deep learning. It began with defining key deep learning concepts and techniques, including neural networks, supervised vs. unsupervised learning, and convolutional neural networks. It then explained how deep learning works by using multiple processing layers to extract higher-level features from data and make predictions. Deep learning has various applications like image recognition and speech recognition. The presentation concluded by discussing how deep learning is inspired by concepts from physics and statistical mechanics.

Image Representation & Descriptors

After an image has been segmented into regions ; the resulting pixels is usually is represented and described in suitable form for further computer processing.

Introduction to CNN

The document discusses convolutional neural networks (CNNs). It begins with an introduction and overview of CNN components like convolution, ReLU, and pooling layers. Convolution layers apply filters to input images to extract features, ReLU introduces non-linearity, and pooling layers reduce dimensionality. CNNs are well-suited for image data since they can incorporate spatial relationships. The document provides an example of building a CNN using TensorFlow to classify handwritten digits from the MNIST dataset.

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.

Introduction to Generative Adversarial Networks (GANs)

Introduction to Generative Adversarial Networks (GANs) by Michał Maj
Full story: https://appsilon.com/satellite-imagery-generation-with-gans/

Introduction to Deep Learning

A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.

Back propagation

The document discusses artificial neural networks and backpropagation. It provides an overview of backpropagation algorithms, including how they were developed over time, the basic methodology of propagating errors backwards, and typical network architectures. It also gives examples of applying backpropagation to problems like robotics, space robots, handwritten digit recognition, and face recognition.

Image segmentation

Unit 3 discusses image segmentation techniques. Similarity based techniques group similar image components, like pixels or frames, for compact representation. Common applications include medical imaging, satellite images, and surveillance. Methods include thresholding and k-means clustering. Segmentation of grayscale images is based on discontinuities in pixel values, detecting edges, or similarities using thresholding, region growing, and splitting/merging. Region growing starts with seed pixels and groups neighboring pixels with similar properties. Region splitting starts with the full image and divides non-homogeneous regions, while region merging combines small similar regions.

Recurrent Neural Network (RNN) | RNN LSTM Tutorial | Deep Learning Course | S...

This presentation on Recurrent Neural Network will help you understand what is a neural network, what are the popular neural networks, why we need recurrent neural network, what is a recurrent neural network, how does a RNN work, what is vanishing and exploding gradient problem, what is LSTM and you will also see a use case implementation of LSTM (Long short term memory). Neural networks used in Deep Learning consists of different layers connected to each other and work on the structure and functions of the human brain. It learns from huge volumes of data and used complex algorithms to train a neural net. The recurrent neural network works on the principle of saving the output of a layer and feeding this back to the input in order to predict the output of the layer. Now lets deep dive into this presentation and understand what is RNN and how does it actually work.
Below topics are explained in this recurrent neural networks tutorial:
1. What is a neural network?
2. Popular neural networks?
3. Why recurrent neural network?
4. What is a recurrent neural network?
5. How does an RNN work?
6. Vanishing and exploding gradient problem
7. Long short term memory (LSTM)
8. Use case implementation of LSTM
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.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
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. Those who complete the course will be able to:
Learn more at: https://www.simplilearn.com/

Image classification using cnn

Image Classification using CNN ,this is abt how the image is process through CNN ,Deep learning ..created by Sumera Hangi

Deep learning presentation

This is a deep learning presentation based on Deep Neural Network. It reviews the deep learning concept, related works and specific application areas.It describes a use case scenario of deep learning and highlights the current trends and research issues of deep learning

Deep Belief Networks

DBNs are graphical models which learn to extract a deep hierarchical representation of the training data.

Machine Learning - Convolutional Neural Network

The document provides an overview of convolutional neural networks (CNNs) for visual recognition. It discusses the basic concepts of CNNs such as convolutional layers, activation functions, pooling layers, and network architectures. Examples of classic CNN architectures like LeNet-5 and AlexNet are presented. Modern architectures such as Inception and ResNet are also discussed. Code examples for image classification using TensorFlow, Keras, and Fastai are provided.

Introduction to object detection

This is part 1/4 of Brodmann17 A-Z Deep Learning based Object Detection meetup given by Assaf Mushinsky

Optimization for Deep Learning

Talk on Optimization for Deep Learning, which gives an overview of gradient descent optimization algorithms and highlights some current research directions.

Deep Learning With Neural Networks

Deep learning uses neural networks, which are systems inspired by the human brain. Neural networks learn patterns from large amounts of data through forward and backpropagation. They are constructed of layers including an input layer, hidden layers, and an output layer. Deep learning can learn very complex patterns and has various applications including image classification, machine translation, and more. Recurrent neural networks are useful for sequential data like text and audio. Convolutional neural networks are widely used in computer vision tasks.

Training Neural Networks

Part 2 of the Deep Learning Fundamentals Series, this session discusses Tuning Training (including hyperparameters, overfitting/underfitting), Training Algorithms (including different learning rates, backpropagation), Optimization (including stochastic gradient descent, momentum, Nesterov Accelerated Gradient, RMSprop, Adaptive algorithms - Adam, Adadelta, etc.), and a primer on Convolutional Neural Networks. The demos included in these slides are running on Keras with TensorFlow backend on Databricks.

Introduction to Recurrent Neural Network

The document provides an introduction to recurrent neural networks (RNNs). It discusses how RNNs differ from feedforward neural networks in that they have internal memory and can use their output from the previous time step as input. This allows RNNs to process sequential data like time series. The document outlines some common RNN types and explains the vanishing gradient problem that can occur in RNNs due to multiplication of small gradient values over many time steps. It discusses solutions to this problem like LSTMs and techniques like weight initialization and gradient clipping.

DL.pdf

This document provides an overview of convolutional neural networks (CNNs). It explains that CNNs are a type of neural network that has been successfully applied to analyzing visual imagery. The document then discusses the motivation and biology behind CNNs, describes common CNN architectures, and explains the key operations of convolution, nonlinearity, pooling, and fully connected layers. It provides examples of CNN applications in computer vision tasks like image classification, object detection, and speech recognition. Finally, it notes several large tech companies that utilize CNNs for features like automatic tagging, photo search, and personalized recommendations.

intro-to-cnn-April_2020.pptx

This document provides an overview of convolutional neural networks (CNNs) and describes a research study that used a two-dimensional heterogeneous CNN (2D-hetero CNN) for mobile health analytics. The study developed a 2D-hetero CNN model to assess fall risk using motion sensor data from 5 sensor locations on participants. The model extracts low-level local features using convolutional layers and integrates them into high-level global features to classify fall risk. The 2D-hetero CNN was evaluated against feature-based approaches and other CNN architectures and performed ablation analysis.

Convolutional Neural Network - CNN | How CNN Works | Deep Learning Course | S...

A Convolutional Neural Network (CNN) is a type of neural network that can process grid-like data like images. It works by applying filters to the input image to extract features at different levels of abstraction. The CNN takes the pixel values of an input image as the input layer. Hidden layers like the convolution layer, ReLU layer and pooling layer are applied to extract features from the image. The fully connected layer at the end identifies the object in the image based on the extracted features. CNNs use the convolution operation with small filter matrices that are convolved across the width and height of the input volume to compute feature maps.

Deep neural networks

Deep learning and neural networks are inspired by biological neurons. Artificial neural networks (ANN) can have multiple layers and learn through backpropagation. Deep neural networks with multiple hidden layers did not work well until recent developments in unsupervised pre-training of layers. Experiments on MNIST digit recognition and NORB object recognition datasets showed deep belief networks and deep Boltzmann machines outperform other models. Deep learning is now widely used for applications like computer vision, natural language processing, and information retrieval.

Deep Learning Explained

This document summarizes Melanie Swan's presentation on deep learning. It began with defining key deep learning concepts and techniques, including neural networks, supervised vs. unsupervised learning, and convolutional neural networks. It then explained how deep learning works by using multiple processing layers to extract higher-level features from data and make predictions. Deep learning has various applications like image recognition and speech recognition. The presentation concluded by discussing how deep learning is inspired by concepts from physics and statistical mechanics.

Image Representation & Descriptors

After an image has been segmented into regions ; the resulting pixels is usually is represented and described in suitable form for further computer processing.

Introduction to CNN

The document discusses convolutional neural networks (CNNs). It begins with an introduction and overview of CNN components like convolution, ReLU, and pooling layers. Convolution layers apply filters to input images to extract features, ReLU introduces non-linearity, and pooling layers reduce dimensionality. CNNs are well-suited for image data since they can incorporate spatial relationships. The document provides an example of building a CNN using TensorFlow to classify handwritten digits from the MNIST dataset.

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.

Introduction to Generative Adversarial Networks (GANs)

Introduction to Generative Adversarial Networks (GANs) by Michał Maj
Full story: https://appsilon.com/satellite-imagery-generation-with-gans/

Introduction to Deep Learning

A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.

Back propagation

The document discusses artificial neural networks and backpropagation. It provides an overview of backpropagation algorithms, including how they were developed over time, the basic methodology of propagating errors backwards, and typical network architectures. It also gives examples of applying backpropagation to problems like robotics, space robots, handwritten digit recognition, and face recognition.

Image segmentation

Unit 3 discusses image segmentation techniques. Similarity based techniques group similar image components, like pixels or frames, for compact representation. Common applications include medical imaging, satellite images, and surveillance. Methods include thresholding and k-means clustering. Segmentation of grayscale images is based on discontinuities in pixel values, detecting edges, or similarities using thresholding, region growing, and splitting/merging. Region growing starts with seed pixels and groups neighboring pixels with similar properties. Region splitting starts with the full image and divides non-homogeneous regions, while region merging combines small similar regions.

Recurrent Neural Network (RNN) | RNN LSTM Tutorial | Deep Learning Course | S...

This presentation on Recurrent Neural Network will help you understand what is a neural network, what are the popular neural networks, why we need recurrent neural network, what is a recurrent neural network, how does a RNN work, what is vanishing and exploding gradient problem, what is LSTM and you will also see a use case implementation of LSTM (Long short term memory). Neural networks used in Deep Learning consists of different layers connected to each other and work on the structure and functions of the human brain. It learns from huge volumes of data and used complex algorithms to train a neural net. The recurrent neural network works on the principle of saving the output of a layer and feeding this back to the input in order to predict the output of the layer. Now lets deep dive into this presentation and understand what is RNN and how does it actually work.
Below topics are explained in this recurrent neural networks tutorial:
1. What is a neural network?
2. Popular neural networks?
3. Why recurrent neural network?
4. What is a recurrent neural network?
5. How does an RNN work?
6. Vanishing and exploding gradient problem
7. Long short term memory (LSTM)
8. Use case implementation of LSTM
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.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
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. Those who complete the course will be able to:
Learn more at: https://www.simplilearn.com/

Image classification using cnn

Image Classification using CNN ,this is abt how the image is process through CNN ,Deep learning ..created by Sumera Hangi

Deep learning presentation

This is a deep learning presentation based on Deep Neural Network. It reviews the deep learning concept, related works and specific application areas.It describes a use case scenario of deep learning and highlights the current trends and research issues of deep learning

Deep Belief Networks

DBNs are graphical models which learn to extract a deep hierarchical representation of the training data.

Machine Learning - Convolutional Neural Network

The document provides an overview of convolutional neural networks (CNNs) for visual recognition. It discusses the basic concepts of CNNs such as convolutional layers, activation functions, pooling layers, and network architectures. Examples of classic CNN architectures like LeNet-5 and AlexNet are presented. Modern architectures such as Inception and ResNet are also discussed. Code examples for image classification using TensorFlow, Keras, and Fastai are provided.

Introduction to object detection

This is part 1/4 of Brodmann17 A-Z Deep Learning based Object Detection meetup given by Assaf Mushinsky

Optimization for Deep Learning

Talk on Optimization for Deep Learning, which gives an overview of gradient descent optimization algorithms and highlights some current research directions.

Deep Learning With Neural Networks

Deep learning uses neural networks, which are systems inspired by the human brain. Neural networks learn patterns from large amounts of data through forward and backpropagation. They are constructed of layers including an input layer, hidden layers, and an output layer. Deep learning can learn very complex patterns and has various applications including image classification, machine translation, and more. Recurrent neural networks are useful for sequential data like text and audio. Convolutional neural networks are widely used in computer vision tasks.

Training Neural Networks

Part 2 of the Deep Learning Fundamentals Series, this session discusses Tuning Training (including hyperparameters, overfitting/underfitting), Training Algorithms (including different learning rates, backpropagation), Optimization (including stochastic gradient descent, momentum, Nesterov Accelerated Gradient, RMSprop, Adaptive algorithms - Adam, Adadelta, etc.), and a primer on Convolutional Neural Networks. The demos included in these slides are running on Keras with TensorFlow backend on Databricks.

Introduction to Recurrent Neural Network

The document provides an introduction to recurrent neural networks (RNNs). It discusses how RNNs differ from feedforward neural networks in that they have internal memory and can use their output from the previous time step as input. This allows RNNs to process sequential data like time series. The document outlines some common RNN types and explains the vanishing gradient problem that can occur in RNNs due to multiplication of small gradient values over many time steps. It discusses solutions to this problem like LSTMs and techniques like weight initialization and gradient clipping.

Convolutional Neural Network - CNN | How CNN Works | Deep Learning Course | S...

Convolutional Neural Network - CNN | How CNN Works | Deep Learning Course | S...

Deep neural networks

Deep neural networks

Deep Learning Explained

Deep Learning Explained

Image Representation & Descriptors

Image Representation & Descriptors

Introduction to CNN

Introduction to CNN

Deep Learning - Convolutional Neural Networks

Deep Learning - Convolutional Neural Networks

Introduction to Generative Adversarial Networks (GANs)

Introduction to Generative Adversarial Networks (GANs)

Introduction to Deep Learning

Introduction to Deep Learning

Back propagation

Back propagation

Image segmentation

Image segmentation

Recurrent Neural Network (RNN) | RNN LSTM Tutorial | Deep Learning Course | S...

Recurrent Neural Network (RNN) | RNN LSTM Tutorial | Deep Learning Course | S...

Image classification using cnn

Image classification using cnn

Deep learning presentation

Deep learning presentation

Deep Belief Networks

Deep Belief Networks

Machine Learning - Convolutional Neural Network

Machine Learning - Convolutional Neural Network

Introduction to object detection

Introduction to object detection

Optimization for Deep Learning

Optimization for Deep Learning

Deep Learning With Neural Networks

Deep Learning With Neural Networks

Training Neural Networks

Training Neural Networks

Introduction to Recurrent Neural Network

Introduction to Recurrent Neural Network

DL.pdf

This document provides an overview of convolutional neural networks (CNNs). It explains that CNNs are a type of neural network that has been successfully applied to analyzing visual imagery. The document then discusses the motivation and biology behind CNNs, describes common CNN architectures, and explains the key operations of convolution, nonlinearity, pooling, and fully connected layers. It provides examples of CNN applications in computer vision tasks like image classification, object detection, and speech recognition. Finally, it notes several large tech companies that utilize CNNs for features like automatic tagging, photo search, and personalized recommendations.

intro-to-cnn-April_2020.pptx

This document provides an overview of convolutional neural networks (CNNs) and describes a research study that used a two-dimensional heterogeneous CNN (2D-hetero CNN) for mobile health analytics. The study developed a 2D-hetero CNN model to assess fall risk using motion sensor data from 5 sensor locations on participants. The model extracts low-level local features using convolutional layers and integrates them into high-level global features to classify fall risk. The 2D-hetero CNN was evaluated against feature-based approaches and other CNN architectures and performed ablation analysis.

Introduction to Convolutional Neural Networks

This document provides an introduction and overview of convolutional neural networks (CNNs). It discusses the key operations in a CNN including convolution, nonlinearity, pooling, and fully connected layers. Convolution extracts features from input images using small filters that preserve spatial relationships between pixels. Pooling reduces the dimensionality of feature maps. The network is trained end-to-end using backpropagation to update filter weights and minimize errors between predicted and true outputs. Visualizing CNNs helps understand how they learn features from images to perform classification.

Deep Learning

This presentation is Part 2 of my September Lisp NYC presentation on Reinforcement Learning and Artificial Neural Nets. We will continue from where we left off by covering Convolutional Neural Nets (CNN) and Recurrent Neural Nets (RNN) in depth.
Time permitting I also plan on having a few slides on each of the following topics:
1. Generative Adversarial Networks (GANs)
2. Differentiable Neural Computers (DNCs)
3. Deep Reinforcement Learning (DRL)
Some code examples will be provided in Clojure.
After a very brief recap of Part 1 (ANN & RL), we will jump right into CNN and their appropriateness for image recognition. We will start by covering the convolution operator. We will then explain feature maps and pooling operations and then explain the LeNet 5 architecture. The MNIST data will be used to illustrate a fully functioning CNN.
Next we cover Recurrent Neural Nets in depth and describe how they have been used in Natural Language Processing. We will explain why gated networks and LSTM are used in practice.
Please note that some exposure or familiarity with Gradient Descent and Backpropagation will be assumed. These are covered in the first part of the talk for which both video and slides are available online.
A lot of material will be drawn from the new Deep Learning book by Goodfellow & Bengio as well as Michael Nielsen's online book on Neural Networks and Deep Learning as well several other online resources.
Bio
Pierre de Lacaze has over 20 years industry experience with AI and Lisp based technologies. He holds a Bachelor of Science in Applied Mathematics and a Master’s Degree in Computer Science.
https://www.linkedin.com/in/pierre-de-lacaze-b11026b/

Introduction to convolutional networks .pptx

Convolutional Neural Networks

build a Convolutional Neural Network (CNN) using TensorFlow in Python

1. The document discusses CNN architecture and concepts like convolution, pooling, and fully connected layers.
2. Convolutional layers apply filters to input images to generate feature maps, capturing patterns like edges. Pooling layers downsample these to reduce parameters.
3. Fully connected layers at the end integrate learned features for classification tasks like image recognition. CNNs exploit spatial structure in images unlike regular neural networks.

Mnist report

This document provides an internship report on classifying handwritten digits using a convolutional neural network. It includes an abstract, introduction on CNNs, explanations of CNN layers including convolution, pooling and fully connected layers. It also discusses padding and applications of CNNs such as computer vision, image recognition and natural language processing.

cnn ppt.pptx

Convolutional neural networks (CNNs) are a type of deep neural network commonly used for analyzing visual imagery. CNNs use various techniques like convolution, ReLU activation, and pooling to extract features from images and reduce dimensionality while retaining important information. CNNs are trained end-to-end using backpropagation to update filter weights and minimize output error. Overall CNN architecture involves an input layer, multiple convolutional and pooling layers to extract features, fully connected layers to classify features, and an output layer. CNNs can be implemented using sequential models in Keras by adding layers, compiling with an optimizer and loss function, fitting on training data over epochs with validation monitoring, and evaluating performance on test data.

Introduction to deep learning

Deep learning (also known as deep structured learning or hierarchical learning) is the application of artificial neural networks (ANNs) to learning tasks that contain more than one hidden layer. Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, partially supervised or unsupervised.

Mnist report ppt

This document is an internship report submitted by Raghunandan J to Eckovation about a project on classifying handwritten digits using a convolutional neural network. It provides an introduction to convolutional neural networks and explains each layer of a CNN including the input, convolutional layer, pooling layer, and fully connected layer. It also gives examples of real-world applications that use artificial neural networks like Google Maps, Google Images, and voice assistants.

Classification of Images Using CNN Model and its Variants

This document presents research on using convolutional neural network (CNN) models for image classification. It introduces CNNs and describes their basic architecture including convolutional layers, pooling layers, ReLU layers, and fully connected layers. It then discusses implementing three CNN configurations on the CIFAR-10 dataset to classify images into 10 classes. The first model is a simple CNN, while the other two add techniques like dropout regularization to prevent overfitting. The results show dropout regularization can significantly improve accuracy, and lower batch sizes may achieve better results than higher batch sizes. The goal is to compare the CNN variants' performance on image classification.

A STUDY OF METHODS FOR TRAINING WITH DIFFERENT DATASETS IN IMAGE CLASSIFICATION

This research developed a training method of Convolutional Neural Network model with multiple datasets to achieve good performance on both datasets. Two different methods of training with two characteristically different datasets with identical categories, one with very clean images and one with real-world data, were proposed and studied. The model used for the study was a neural network derived from ResNet. Mixed training was shown to produce the best accuracies for each dataset when the dataset is mixed into the training set at the highest proportion, and the best combined performance when the realworld dataset was mixed in at a ratio of around 70%. This ratio produced a top-1 combined performance of 63.8% (no mixing produced 30.8%) and a top-3 combined performance of 83.0% (no mixing produced 55.3%). This research also showed that iterative training has a worse combined performance than mixed training due to the issue of fast forgetting.

A Fully Progressive approach to Single image super-resolution

This Slide is an intro for the "fully progressive approach to single image super-resolution" Paper
link: https://github.com/fperazzi/proSR

Introduction to computer vision

This document provides an introduction to computer vision with convoluted neural networks. It discusses what computer vision aims to address, provides a brief overview of neural networks and their basic building blocks. It then covers the history and evolution of convolutional neural networks, how and why they work on digital images, their limitations, and applications like object detection. Examples are provided of early CNNs from the 1980s and 1990s and recent advancements through the 2010s that improved accuracy, including deeper networks, inception modules, residual connections, and efforts to increase performance like MobileNets. Training deep CNNs requires large datasets and may take weeks, but pre-trained networks can be fine-tuned for new tasks.

Introduction to computer vision with Convoluted Neural Networks

Introduction to computer vision with Convoluted Neural Networks - going over history of CNNs, describing basic concepts such as convolution and discussing applications of computer vision and image recognition technologies

Scene understanding

The document describes a scene understanding model that generates natural language descriptions of images. It discusses how humans understand scenes, then outlines the key components of the model: convolutional neural networks to extract image features, transfer learning from pre-trained models, and recurrent neural networks to generate captions. The presentation includes details on CNNs, LSTMs, training the model on Flickr 30k images and captions, and a demonstration of captions generated for sample images of varying complexity.

Handwritten Digit Recognition using Convolutional Neural Networks

This document discusses using a convolutional neural network called LeNet to perform handwritten digit recognition on the MNIST dataset. It begins with an abstract that outlines using LeNet, a type of convolutional network, to accurately classify handwritten digits from 0 to 9. It then provides background on convolutional networks and how they can extract and utilize features from images to classify patterns with translation and scaling invariance. The document implements LeNet using the Keras deep learning library in Python to classify images from the MNIST dataset, which contains labeled images of handwritten digits. It analyzes the architecture of LeNet and how convolutional and pooling layers are used to extract features that are passed to fully connected layers for classification.

CNN.pptx

The document discusses Convolutional Neural Networks (CNNs). It explains that CNNs are a type of neural network that use convolutional operations in at least one layer. CNNs are well-suited for image classification and segmentation problems. The key layers in a CNN are convolutional layers, pooling layers, flattening layers, and fully connected layers. Convolutional layers act as feature extractors, pooling layers reduce spatial size, flattening layers transform pooled features into a vector, and fully connected layers are for classification.

Deep Neural Network DNN.docx

Deep convolutional neural networks (DCNNs) are a type of neural network commonly used for analyzing visual imagery. They work by using convolutional layers that extract features from images using small filters that slide across the input. Pooling layers then reduce the spatial size of representations to reduce computation. Multiple convolutional and pooling layers are followed by fully connected layers that perform classification. Key aspects of DCNNs include activation functions, dropout layers, hyperparameters like filter size and number of layers, and training for many epochs with techniques like early stopping.

Convolutional Neural Networks

CNNs are a type of deep learning algorithm used for computer vision tasks. They use convolutional and pooling layers to extract features from image data. CNNs apply multiple filters to input images to generate feature maps, which are then passed through activation functions to introduce nonlinearity. The features are then pooled and fed into fully connected layers for classification. Common CNN architectures include LeNet-5, AlexNet, VGG16, GoogLeNet, and ResNet, with more recent models featuring deeper networks and improved training techniques.

DL.pdf

DL.pdf

intro-to-cnn-April_2020.pptx

intro-to-cnn-April_2020.pptx

Introduction to Convolutional Neural Networks

Introduction to Convolutional Neural Networks

Deep Learning

Deep Learning

Introduction to convolutional networks .pptx

Introduction to convolutional networks .pptx

build a Convolutional Neural Network (CNN) using TensorFlow in Python

build a Convolutional Neural Network (CNN) using TensorFlow in Python

Mnist report

Mnist report

cnn ppt.pptx

cnn ppt.pptx

Introduction to deep learning

Introduction to deep learning

Mnist report ppt

Mnist report ppt

Classification of Images Using CNN Model and its Variants

Classification of Images Using CNN Model and its Variants

A STUDY OF METHODS FOR TRAINING WITH DIFFERENT DATASETS IN IMAGE CLASSIFICATION

A STUDY OF METHODS FOR TRAINING WITH DIFFERENT DATASETS IN IMAGE CLASSIFICATION

A Fully Progressive approach to Single image super-resolution

A Fully Progressive approach to Single image super-resolution

Introduction to computer vision

Introduction to computer vision

Introduction to computer vision with Convoluted Neural Networks

Introduction to computer vision with Convoluted Neural Networks

Scene understanding

Scene understanding

Handwritten Digit Recognition using Convolutional Neural Networks

Handwritten Digit Recognition using Convolutional Neural Networks

CNN.pptx

CNN.pptx

Deep Neural Network DNN.docx

Deep Neural Network DNN.docx

Convolutional Neural Networks

Convolutional Neural Networks

zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...

Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257

“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...

“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...Edge AI and Vision Alliance

For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency

Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.

"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor Ivaniuk

At this talk we will discuss DDoS protection tools and best practices, discuss network architectures and what AWS has to offer. Also, we will look into one of the largest DDoS attacks on Ukrainian infrastructure that happened in February 2022. We'll see, what techniques helped to keep the web resources available for Ukrainians and how AWS improved DDoS protection for all customers based on Ukraine experience

Must Know Postgres Extension for DBA and Developer during Migration

Mydbops Opensource Database Meetup 16
Topic: Must-Know PostgreSQL Extensions for Developers and DBAs During Migration
Speaker: Deepak Mahto, Founder of DataCloudGaze Consulting
Date & Time: 8th June | 10 AM - 1 PM IST
Venue: Bangalore International Centre, Bangalore
Abstract: Discover how PostgreSQL extensions can be your secret weapon! This talk explores how key extensions enhance database capabilities and streamline the migration process for users moving from other relational databases like Oracle.
Key Takeaways:
* Learn about crucial extensions like oracle_fdw, pgtt, and pg_audit that ease migration complexities.
* Gain valuable strategies for implementing these extensions in PostgreSQL to achieve license freedom.
* Discover how these key extensions can empower both developers and DBAs during the migration process.
* Don't miss this chance to gain practical knowledge from an industry expert and stay updated on the latest open-source database trends.
Mydbops Managed Services specializes in taking the pain out of database management while optimizing performance. Since 2015, we have been providing top-notch support and assistance for the top three open-source databases: MySQL, MongoDB, and PostgreSQL.
Our team offers a wide range of services, including assistance, support, consulting, 24/7 operations, and expertise in all relevant technologies. We help organizations improve their database's performance, scalability, efficiency, and availability.
Contact us: info@mydbops.com
Visit: https://www.mydbops.com/
Follow us on LinkedIn: https://in.linkedin.com/company/mydbops
For more details and updates, please follow up the below links.
Meetup Page : https://www.meetup.com/mydbops-databa...
Twitter: https://twitter.com/mydbopsofficial
Blogs: https://www.mydbops.com/blog/
Facebook(Meta): https://www.facebook.com/mydbops/

Apps Break Data

How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?

Fueling AI with Great Data with Airbyte Webinar

This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.

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

Taking AI to the Next Level in Manufacturing.pdf

Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.

Introduction of Cybersecurity with OSS at Code Europe 2024

I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.

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.

Christine's Supplier Sourcing Presentaion.pptx

How I source suppliers

Columbus Data & Analytics Wednesdays - June 2024

Columbus Data & Analytics Wednesdays, June 2024 with Maria Copot 20

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.

Your One-Stop Shop for Python Success: Top 10 US Python Development Providers

Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.

"Scaling RAG Applications to serve millions of users", Kevin Goedecke

How we managed to grow and scale a RAG application from zero to thousands of users in 7 months. Lessons from technical challenges around managing high load for LLMs, RAGs and Vector databases.

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.

Monitoring and Managing Anomaly Detection on OpenShift.pdf

Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.

inQuba Webinar Mastering Customer Journey Management with Dr Graham Hill

HERE IS YOUR WEBINAR CONTENT! 'Mastering Customer Journey Management with Dr. Graham Hill'. We hope you find the webinar recording both insightful and enjoyable.
In this webinar, we explored essential aspects of Customer Journey Management and personalization. Here’s a summary of the key insights and topics discussed:
Key Takeaways:
Understanding the Customer Journey: Dr. Hill emphasized the importance of mapping and understanding the complete customer journey to identify touchpoints and opportunities for improvement.
Personalization Strategies: We discussed how to leverage data and insights to create personalized experiences that resonate with customers.
Technology Integration: Insights were shared on how inQuba’s advanced technology can streamline customer interactions and drive operational efficiency.

JavaLand 2024: Application Development Green Masterplan

My presentation slides I used at JavaLand 2024

zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...

zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...

“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...

“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...

Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency

Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency

"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor Ivaniuk

"Frontline Battles with DDoS: Best practices and Lessons Learned", Igor Ivaniuk

Must Know Postgres Extension for DBA and Developer during Migration

Must Know Postgres Extension for DBA and Developer during Migration

Apps Break Data

Apps Break Data

Fueling AI with Great Data with Airbyte Webinar

Fueling AI with Great Data with Airbyte Webinar

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

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

Taking AI to the Next Level in Manufacturing.pdf

Taking AI to the Next Level in Manufacturing.pdf

Introduction of Cybersecurity with OSS at Code Europe 2024

Introduction of Cybersecurity with OSS at Code Europe 2024

A Deep Dive into ScyllaDB's Architecture

A Deep Dive into ScyllaDB's Architecture

Christine's Supplier Sourcing Presentaion.pptx

Christine's Supplier Sourcing Presentaion.pptx

Columbus Data & Analytics Wednesdays - June 2024

Columbus Data & Analytics Wednesdays - June 2024

Astute Business Solutions | Oracle Cloud Partner |

Astute Business Solutions | Oracle Cloud Partner |

Your One-Stop Shop for Python Success: Top 10 US Python Development Providers

Your One-Stop Shop for Python Success: Top 10 US Python Development Providers

"Scaling RAG Applications to serve millions of users", Kevin Goedecke

"Scaling RAG Applications to serve millions of users", Kevin Goedecke

Mutation Testing for Task-Oriented Chatbots

Mutation Testing for Task-Oriented Chatbots

Monitoring and Managing Anomaly Detection on OpenShift.pdf

Monitoring and Managing Anomaly Detection on OpenShift.pdf

inQuba Webinar Mastering Customer Journey Management with Dr Graham Hill

inQuba Webinar Mastering Customer Journey Management with Dr Graham Hill

JavaLand 2024: Application Development Green Masterplan

JavaLand 2024: Application Development Green Masterplan

- 2. Data Science
- 3. Applications
- 6. Neural Network Artificial Neural Network (ANN) Regression and classification Convolutional Neural Network (CNN) Computer Vision Recurrent Neural Network (RNN) Time series analysis
- 7. Artificial Neural Network Single layer perceptron Multi-layer perceptron
- 9. Convolutional Neural Networks (CNNs) learns multi-level features and classifier in a joint fashion and performs much better than traditional approaches for various image classification and segmentation problems. Introduction
- 10. Low Level Features Mid Level Features Output (e.g. car, train) High Level Features Trainable Classifier CNN – What do they learn? Convolutional layers Fully connected layers
- 11. There are four main components in the CNN: 1. Convolution 2. Non-Linearity 3. Pooling or Sub Sampling 4. Classification (Fully Connected Layer) CNN - Components
- 12. Input • An Image is a matrix of pixel values. • If we consider a gray scale image, the value of each pixel in the matrix will range from 0 to 255. • If we consider an RGB image, each pixel will have the combined values of R, G and B.
- 13. Convolution The primary purpose of Convolution in case of a CNN is to extract features from the input image. Convolved Feature / Activation Map / Feature Map Image Filter / Kernel / Feature detector
- 14. Convolution…
- 15. • The size of the output volume is controlled by three parameters that we need to decide before the convolution step is performed: Depth: Depth corresponds to the number of filters we use for the convolution operation. Stride: Stride is the number of pixels by which we slide our filter matrix over the input matrix. Zero-padding: Sometimes, it is convenient to pad the input matrix with zeros around the border, so that we can apply the filter to bordering elements of our input image matrix. • With zero-padding wide convolution • Without zero-padding narrow convolution Convolution...
- 16. • Replaces all negative pixel values in the feature map by zero. • The purpose of ReLU is to introduce non- linearity in CNN, since most of the real-world data would be non-linear. • Other non-linear functions such as tanh (-1,1) or sigmoid (0,1) can also be used instead of ReLU (0,input). Non-Linearity (ReLU)
- 17. Pooling Reduces the dimensionality of each feature map but retains the most important information. Pooling can be of different types: Max, Average, Sum etc. 2×2 region
- 18. • Together these layers extract the useful features from the images. • The output from the convolutional and pooling layers represent high-level features of the input image. Story so far High-level features
- 19. • A traditional Multi-Layer Perceptron. • The term “Fully Connected” implies that every neuron in the previous layer is connected to every neuron on the next layer. • Their activations can hence be computed with a matrix multiplication followed by a bias offset. • The purpose of the Fully Connected layer is to use the high-level features for classifying the input image into various classes based on the training dataset. Fully Connected Layer
- 23. Putting it all together – Training using Backpropagation • Step 1: We initialize all filters and parameters / weights with random values. • Step 2: The network takes a training image as input, goes through the forward propagation step (convolution, ReLU and pooling operations along with forward propagation in the Fully Connected layer) and finds the output probabilities for each class. • Let’s say the output probabilities for the boat image above are [0.2, 0.4, 0.1, 0.3]. • Since weights are randomly assigned for the first training example, output probabilities are also random. • Step 3: Calculate the total error at the output layer (summation over all 4 classes).
- 24. • Step 4: Use Backpropagation to calculate the gradients of the error with respect to all weights in the network and use gradient descent to update all filter values/ weights and parameter values to minimize the output error. • The weights are adjusted in proportion to their contribution to the total error. • When the same image is input again, output probabilities might now be [0.1, 0.1, 0.7, 0.1], which is closer to the target vector [0, 0, 1, 0]. • This means that the network has learnt to classify this particular image correctly by adjusting its weights / filters such that the output error is reduced. • Parameters like number of filters, filter sizes, architecture of the network etc. have all been fixed before Step 1 and do not change during training process – only the values of the filter matrix and connection weights get updated. • Step 5: Repeat steps 2-4 with all images in the training set.
- 26. Year CNN Architecture Developed By 1998 LeNet Yann LeCun et al. 2012 AlexNet Alex Krizhevsky, Geoffrey Hinton, and Ilya Sutskever 2013 ZFNet Matthew Zeiler and Rob Fergus 2014 GoogleNet Google 2014 VGGNet Simonyan and Zisserman 2015 ResNet Kaiming He 2017 DenseNet Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q. Weinberger
- 28. Recurrent Neural Network - RNN
- 29. What are the differences among ANN, CNN and RNN?
- 30. Visualizing a CNN • Adam Harley created amazing visualizations of a Convolutional Neural Network trained on the MNIST Database of handwritten digits • 2D Visualisation of a CNN - http://scs.ryerson.ca/~aharley/vis/conv/flat.html • 3D Visualisation of a CNN - http://scs.ryerson.ca/~aharley/vis/conv/
- 31. Thank You!