1) The document provides a tutorial on Keras with the objective of introducing deep learning and how to use the Keras library in Python.
2) The tutorial covers installing Keras, configuring backends, an overview of deep learning concepts, and how to build models using Keras modules, layers, model compilation, CNNs, and LSTMs.
3) Examples are provided on creating simple Keras models using different layers like Dense, Dropout, and Flatten to demonstrate how models can be built for classification tasks.
Keras is a high level framework that runs on top of AI library such as Tensorflow, Theano, or CNTK. The key feature of Keras is that it allow to switch out the underlying library without performing any code changes. Keras contains commonly used neural-network building blocks such as layers, optimizer, activation functions etc and keras has support for convolutional and recurrent neural networks. In addition keras contains datasets and some pre-trained deep learnig applications that make it easier to learn for beginners. Essentially Keras is democrasting deep learning by reducing barrier into deep learning.
An introduction to Keras, a high-level neural networks library written in Python. Keras makes deep learning more accessible, is fantastic for rapid protyping, and can run on top of TensorFlow, Theano, or CNTK. These slides focus on examples, starting with logistic regression and building towards a convolutional neural network.
The presentation was given at the Austin Deep Learning meetup: https://www.meetup.com/Austin-Deep-Learning/events/237661902/
Keras is a high-level neural networks API, written in Python and capable of running on top of either TensorFlow, CNTK or Theano.
We can easily build a model and train it using keras very easily with few lines of code.The steps to train the model is described in the presentation.
Use Keras if you need a deep learning library that:
-Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility).
-Supports both convolutional networks and recurrent networks, as well as combinations of the two.
-Runs seamlessly on CPU and GPU.
Keras Tutorial For Beginners | Creating Deep Learning Models Using Keras In P...Edureka!
** AI & Deep Learning Training: https://www.edureka.co/ai-deep-learning-with-tensorflow ** )
This Edureka Tutorial on "Keras Tutorial" (Deep Learning Blog Series: https://goo.gl/4zxMfU) provides you a quick and insightful tutorial on the working of Keras along with an interesting use-case! We will be checking out the following topics:
Agenda:
What is Keras?
Who makes Keras?
Who uses Keras?
What Makes Keras special?
Working principle of Keras
Keras Models
Understanding Execution
Implementing a Neural Network
Use-Case with Keras
Coding in Colaboratory
Session in a minute
Check out our Deep Learning blog series: https://bit.ly/2xVIMe1
Check out our complete Youtube playlist here: https://bit.ly/2OhZEpz
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Tijmen Blankenvoort, co-founder Scyfer BV, presentation at Artificial Intelligence Meetup 15-1-2014. Introduction into Neural Networks and Deep Learning.
Keras is a high level framework that runs on top of AI library such as Tensorflow, Theano, or CNTK. The key feature of Keras is that it allow to switch out the underlying library without performing any code changes. Keras contains commonly used neural-network building blocks such as layers, optimizer, activation functions etc and keras has support for convolutional and recurrent neural networks. In addition keras contains datasets and some pre-trained deep learnig applications that make it easier to learn for beginners. Essentially Keras is democrasting deep learning by reducing barrier into deep learning.
An introduction to Keras, a high-level neural networks library written in Python. Keras makes deep learning more accessible, is fantastic for rapid protyping, and can run on top of TensorFlow, Theano, or CNTK. These slides focus on examples, starting with logistic regression and building towards a convolutional neural network.
The presentation was given at the Austin Deep Learning meetup: https://www.meetup.com/Austin-Deep-Learning/events/237661902/
Keras is a high-level neural networks API, written in Python and capable of running on top of either TensorFlow, CNTK or Theano.
We can easily build a model and train it using keras very easily with few lines of code.The steps to train the model is described in the presentation.
Use Keras if you need a deep learning library that:
-Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility).
-Supports both convolutional networks and recurrent networks, as well as combinations of the two.
-Runs seamlessly on CPU and GPU.
Keras Tutorial For Beginners | Creating Deep Learning Models Using Keras In P...Edureka!
** AI & Deep Learning Training: https://www.edureka.co/ai-deep-learning-with-tensorflow ** )
This Edureka Tutorial on "Keras Tutorial" (Deep Learning Blog Series: https://goo.gl/4zxMfU) provides you a quick and insightful tutorial on the working of Keras along with an interesting use-case! We will be checking out the following topics:
Agenda:
What is Keras?
Who makes Keras?
Who uses Keras?
What Makes Keras special?
Working principle of Keras
Keras Models
Understanding Execution
Implementing a Neural Network
Use-Case with Keras
Coding in Colaboratory
Session in a minute
Check out our Deep Learning blog series: https://bit.ly/2xVIMe1
Check out our complete Youtube playlist here: https://bit.ly/2OhZEpz
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Tijmen Blankenvoort, co-founder Scyfer BV, presentation at Artificial Intelligence Meetup 15-1-2014. Introduction into Neural Networks and Deep Learning.
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/
Basic concept of Deep Learning with explaining its structure and backpropagation method and understanding autograd in PyTorch. (+ Data parallism in PyTorch)
Interest in Deep Learning has been growing in the past few years. With advances in software and hardware technologies, Neural Networks are making a resurgence. With interest in AI based applications growing, and companies like IBM, Google, Microsoft, NVidia investing heavily in computing and software applications, it is time to understand Deep Learning better!
In this workshop, we will discuss the basics of Neural Networks and discuss how Deep Learning Neural networks are different from conventional Neural Network architectures. We will review a bit of mathematics that goes into building neural networks and understand the role of GPUs in Deep Learning. We will also get an introduction to Autoencoders, Convolutional Neural Networks, Recurrent Neural Networks and understand the state-of-the-art in hardware and software architectures. Functional Demos will be presented in Keras, a popular Python package with a backend in Theano and Tensorflow.
The release of TensorFlow 2.0 comes with a significant number of improvements over its 1.x version, all with a focus on ease of usability and a better user experience. We will give an overview of what TensorFlow 2.0 is and discuss how to get started building models from scratch using TensorFlow 2.0’s high-level api, Keras. We will walk through an example step-by-step in Python of how to build an image classifier. We will then showcase how to leverage a transfer learning to make building a model even easier! With transfer learning, we can leverage other pretrained models such as ImageNet to drastically speed up the training time of our model. TensorFlow 2.0 makes this incredibly simple to do.
Recurrent Neural Networks hold great promise as general sequence learning algorithms. As such, they are a very promising tool for text analysis. However, outside of very specific use cases such as handwriting recognition and recently, machine translation, they have not seen wide spread use. Why has this been the case?
In this presentation, we will first introduce RNNs as a concept. Then we will sketch how to implement them and cover the tricks necessary to make them work well. With the basics covered, we will investigate using RNNs as general text classification and regression models, examining where they succeed and where they fail compared to more traditional text analysis models. A straightforward open-source Python and Theano library for training RNNs with a scikit-learn style interface will be introduced and we’ll see how to use it through a tutorial on a real world text dataset
https://telecombcn-dl.github.io/dlmm-2017-dcu/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
Recurrent Neural Network
ACRRL
Applied Control & Robotics Research Laboratory of Shiraz University
Department of Power and Control Engineering, Shiraz University, Fars, Iran.
Mohammad Sabouri
https://sites.google.com/view/acrrl/
This is a presentation I gave as a short overview of LSTMs. The slides are accompanied by two examples which apply LSTMs to Time Series data. Examples were implemented using Keras. See links in slide pack.
Online Machine Learning on Streaming Data With River and Bytewax With Zander ...HostedbyConfluent
Online Machine Learning on Streaming Data With River and Bytewax With Zander Matheson | Current 2022
In this session we will look at how to leverage the Python libraries River and Bytewax to build streaming applications on Kafka that use online machine learning techniques.
What is Deep Learning
Rise of Deep Learning
Phases of Deep Learning - Training and Inference
AI & Limitations of Deep Learning
Apache MXNet History, Apache MXNet concepts
How to use Apache MXNet and Spark together for Distributed Inference.
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/
Basic concept of Deep Learning with explaining its structure and backpropagation method and understanding autograd in PyTorch. (+ Data parallism in PyTorch)
Interest in Deep Learning has been growing in the past few years. With advances in software and hardware technologies, Neural Networks are making a resurgence. With interest in AI based applications growing, and companies like IBM, Google, Microsoft, NVidia investing heavily in computing and software applications, it is time to understand Deep Learning better!
In this workshop, we will discuss the basics of Neural Networks and discuss how Deep Learning Neural networks are different from conventional Neural Network architectures. We will review a bit of mathematics that goes into building neural networks and understand the role of GPUs in Deep Learning. We will also get an introduction to Autoencoders, Convolutional Neural Networks, Recurrent Neural Networks and understand the state-of-the-art in hardware and software architectures. Functional Demos will be presented in Keras, a popular Python package with a backend in Theano and Tensorflow.
The release of TensorFlow 2.0 comes with a significant number of improvements over its 1.x version, all with a focus on ease of usability and a better user experience. We will give an overview of what TensorFlow 2.0 is and discuss how to get started building models from scratch using TensorFlow 2.0’s high-level api, Keras. We will walk through an example step-by-step in Python of how to build an image classifier. We will then showcase how to leverage a transfer learning to make building a model even easier! With transfer learning, we can leverage other pretrained models such as ImageNet to drastically speed up the training time of our model. TensorFlow 2.0 makes this incredibly simple to do.
Recurrent Neural Networks hold great promise as general sequence learning algorithms. As such, they are a very promising tool for text analysis. However, outside of very specific use cases such as handwriting recognition and recently, machine translation, they have not seen wide spread use. Why has this been the case?
In this presentation, we will first introduce RNNs as a concept. Then we will sketch how to implement them and cover the tricks necessary to make them work well. With the basics covered, we will investigate using RNNs as general text classification and regression models, examining where they succeed and where they fail compared to more traditional text analysis models. A straightforward open-source Python and Theano library for training RNNs with a scikit-learn style interface will be introduced and we’ll see how to use it through a tutorial on a real world text dataset
https://telecombcn-dl.github.io/dlmm-2017-dcu/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
Recurrent Neural Network
ACRRL
Applied Control & Robotics Research Laboratory of Shiraz University
Department of Power and Control Engineering, Shiraz University, Fars, Iran.
Mohammad Sabouri
https://sites.google.com/view/acrrl/
This is a presentation I gave as a short overview of LSTMs. The slides are accompanied by two examples which apply LSTMs to Time Series data. Examples were implemented using Keras. See links in slide pack.
Online Machine Learning on Streaming Data With River and Bytewax With Zander ...HostedbyConfluent
Online Machine Learning on Streaming Data With River and Bytewax With Zander Matheson | Current 2022
In this session we will look at how to leverage the Python libraries River and Bytewax to build streaming applications on Kafka that use online machine learning techniques.
What is Deep Learning
Rise of Deep Learning
Phases of Deep Learning - Training and Inference
AI & Limitations of Deep Learning
Apache MXNet History, Apache MXNet concepts
How to use Apache MXNet and Spark together for Distributed Inference.
A Deeper Dive into Apache MXNet - March 2017 AWS Online Tech TalksAmazon Web Services
Deep learning continues to push the state of the art in domains such as computer vision, natural language understanding and recommendation engines. One of the key reasons for this progress is the availability of highly flexible and developer friendly deep learning frameworks. Apache MXNet is a fully-featured, flexibly-programmable and ultra-scalable deep learning framework supporting innovative deep models including convolutional neural networks (CNNs), and long short-term memory networks (LSTMs). This Tech Talk will show you how to launch the deep learning cloud formation template and deploy the deep learning AMI to train your own deep neural network, using MNIST, to recognize handwritten digits and test it for accuracy.
Learning Objectives:
- Learn about the features and benefits of Apache MXNet
- Learn about the deep learning AMIs with the tools you need for DL
- Learn how to train a neural network using MXNet"
A Deeper Dive into Apache MXNet - March 2017 AWS Online Tech TalksAmazon Web Services
Deep learning continues to push the state of the art in domains such as computer vision, natural language understanding and recommendation engines. One of the key reasons for this progress is the availability of highly flexible and developer friendly deep learning frameworks. Apache MXNet is a fully-featured, flexibly-programmable and ultra-scalable deep learning framework supporting innovative deep models including convolutional neural networks (CNNs), and long short-term memory networks (LSTMs). This Tech Talk will show you how to launch the deep learning cloud formation template and deploy the deep learning AMI to train your own deep neural network, using MNIST, to recognize handwritten digits and test it for accuracy.
Learning Objectives:
- Learn about the features and benefits of Apache MXNet
- Learn about the deep learning AMIs with the tools you need for DL
- Learn how to train a neural network using MXNet
Synthetic dialogue generation with Deep LearningS N
A walkthrough of a Deep Learning based technique which would generate TV scripts using Recurrent Neural Network. The model will generate a completely new TV script for a scene, after being training from a dataset. One will learn the concepts around RNN, NLP and various deep learning techniques.
Technologies to be used:
Python 3, Jupyter, TensorFlow
Source code: https://github.com/syednasar/talks/tree/master/synthetic-dialog
Transfer learning (TL) is a research problem in machine learning (ML) that focuses on applying knowledge gained while solving one task to a related task
Deep learning continues to push the state of the art in domains such as computer vision, natural language understanding and recommendation engines. One of the key reasons for this progress is the availability of highly flexible and developer friendly deep learning frameworks. During this workshop, we will provide a short background on Deep Learning focusing on relevant application domains and an introduction to the powerful and scalable Deep Learning framework, Apache MXNet. At the end of this tutorial you’ll be able to train your own deep neural network, fine tune existing state of the art models for image and object recognition. We’ll also deep dive on setting up your deep learning infrastructure on AWS and model deployment on AWS Lambda.
Machine Learning with ML.NET and Azure - Andy CrossAndrew Flatters
ML.NET is an open source, machine learning framework built in .NET and runs on Windows, Linux and macOS. It allows developers to integrate custom machine learning into their applications without any prior expertise in developing or tuning machine learning models. Enhance your .NET apps with sentiment analysis, price prediction, fraud detection and more using custom models built with ML.NET
About Andy Cross
Andy Cross (@andyelastacloud) is a co-founder of Elastacloud, an Azure Insider, co-founder of the UK London Azure User Group, an Azure MVP and a Microsoft Regional Director. An international speaker, Andy has lead teams building the largest Hadoop and HDInsight specialist deployments on Azure.
His passion for embedded software and high performance compute clusters gives him a unique insight into a sphere of computation from the very small and resource constrained to the massively scalable, limitless potential of the cloud.
Distributed Inference on Large Datasets Using Apache MXNet and Apache Spark ...Databricks
Deep Learning has become ubiquitous with abundance of data, commoditization of compute and storage. Pre-trained models are readily available for many use-cases. Distributed Inference has many applications such as pre-computing results offline, backfilling historic data with predictions from state-of-the-art models, etc.Inference on large scale datasets comes with many challenges prevalent in distributed data processing.
Attendees will learn how to efficiently run deep learning prediction on large data sets, leveraging Apache Spark and Apache MXNet (incubating).
In this session, we’ll cover core Deep Learning Concepts such as:
Types of Learning, a) Supervised Learning b) Unsupervised Learning c) Active Learning d) Reinforcement Learning
Supervised Learning types – classification, regression, Image classification
Types of Neural Networks – Feed forward Networks, CNNs, RNNs, GANs * Apache MXNet(Incubating) Deep Learning Framework. MXNet concepts ie., NDArray, Symbolic APIs and Module APIs. MXNet Gluon APIs * Distributed Inference using Apache MXNet and Apache Spark on Amazon EMR.
In this section, I will cover some of the use-cases of Distributed Inference, the challenges associated with running distributed Inference.
Distributed Inference with MXNet and SparkApache MXNet
Deep Learning has become ubiquitous with abundance of data, commoditization of compute and storage. Pre-trained models are readily available for many use-cases. Distributed Inference has many applications such as pre-computing results offline, backfilling historic data with predictions from state-of-the-art models, etc.,. Inference on large scale datasets comes with many challenges prevalent in distributed data processing. This presentation will show how to efficiently run deep learning prediction on large data sets, leveraging Apache Spark and Apache MXNet (incubating).
Build a simple image recognition system with tensor flowDebasisMohanty37
A perfect working model to detect mnist dataset using TensorFlow.
Dataset:
http://yann.lecun.com/exdb/mnist/
For code check the below GitHub links:
https://github.com/Jitudebz/psychic-pancake
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Show drafts
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
1. TUTORIAL ON KERAS
STUDENT NAME : MAHMUT KAMALAK
LECTURE : INTRODUCTION TO PYTHON
PROFESSOR : PROF. DR. BAHADIR AKTUĞ
ID NUMBER :16290155
2. OBJECTIVE OF THE TUTORIAL
I decided to prepare a tutorial about Keras , because I am
interested in Deep Learning for months ,and I am working on a
project which is about ECG arythmia classification. In my
opinion; before start to learn Keras, there are some pre-requires
that are deep learning theory, basic python knowledge, basic
linear algebra ,basic statisctics and probabilty. I strongly advice;
after supply pre-requires , new learners should go deep projects
and articles.These days, Deep Learning is so popular by
researchers and companies in many field because of the
capability of artifial intelligence. Especially I would like to say
deep learning will take amazing responsibility of diognastic and
imaging on medical ındustry. From my view , Deep Learning has
a deep way but, there are huge magical things.
3. OVERVIEW OF THE TUTORIAL
A. Introduction
B. Installation
C. Backend Configuration
D. Overview of Deep Learning
E. Deep Learning with Keras
F. Modules
G. Layers
H. Model Compilation
I. Convolution Neural Network
J. LSTM – RNN
K. Applications
4. A.INTRODUCTION
What is Keras ?
• Keras is a deep learning libriary for Python.
• Keras is a so effective to build deep learning model and
training the models.
• This libriary makes less time-consuming to do practice for
researchers.
• It is capable of running on top of TensorFlow.
• It was developed as part of the research effort of project
ONEIROS (Open-ended Neuro-Electronic Intelligent Robot
Operating System), and its primary author and maintainer is
François Chollet, a Google engineer.
5. A.INTRODUCTION
WHY USE KERAS ?
• Keras allows to run the code without any change on both CPU
and GPU.
• High-level neural networks API.
• Useful for fast prototyping, ignoring the details of implenting
backprop or writing optimization procedure.
• Provides to work Convolution , Recurrent layer and combination
of both.
• Most of architecture is designed by using this framework.
• Keras and Python gives a amazing power to developer as
6. A.INTRODUCTION
• Keras is usually used for small datasets as it is comparitively
slower. On the other hand, TensorFlow and PyTorch are used
for high performance models and large datasets that require
fast execution.
• We can differentiate Keras like that Keras tops the list
followed by TensorFlow.Keras gained amazing popularity due
to its simplicity.
8. B.INSTALLATION
1) PREREQUISITES
You must satisfy the following requirements:
• Any kind of OS (Windows, Linux or Mac)
• Python version 3.5 or higher.
• Keras is python based neural network library so python must be
installed on your machine. If python is properly installed on
your machine, then open your terminal and type python, you
can check as specified below :
9. B.INSTALLATION
Step 1: Create virtual environment
• Virtualenv is used to manage Python packages for different
projects. This will be helpful to avoid breaking the packages
installed in the other environments. So, it is always recommended
to use a virtual environment while developing Python
applications.
• Linux or mac OS users, go to your project root directory and type
the below command to create virtual environment :
« python3 -m venv kerasenv «
• Windows user can use the below command :
« py -m venv keras »
10. B.INSTALLATION
Step 2: Activate the environment
This step will configure python and pip executables in your shell
path.
Linux/Mac OS
• Now we have created a virtual environment named “kerasvenv”.
Move to the folder and type the below command,
«$ cd kerasvenv
kerasvenv $ source bin/activate «
Windows users move inside the “kerasenv” folder and type the
below command:
«.envScriptsactivate «
11. B.INSTALLATION
Step 3: Python libraries
Keras depends on the following python libraries : Numpy , Pandas
, Scikit-learn , Matplotlib , Scipy , Seaborn.
When you installed the keras, u have already installed these
libriaries on your computer.But If there is a problem about any
libriary , we can install these one by one . As below structure
which is on command window :
« pip install ‘name of libriary’ «
12. B.INSTALLATION
• Example for Step-3 : If you have already installed , you will get
:
If you havent done before, you will get :
13. B.INSTALLATION
Step 4 : Keras Installation Using Python
As of now, we have completed basic requirements for the
installtion of Keras. Now, install the Keras using same procedure
on the command as specified below:
« pip install keras «
*** Quit virtual environment ***
After finishing all your changes in your project, then simply run
the below command to quit the environment:
« deactivate »
14. C.BACKEND CONFIGURATION
• This step explains Keras backend implementations TensorFlow
and Theano .
TensorFlow
TensorFlow is an open source machine learning library used for
numerical computational tasks developed by Google. Keras is a
high level API built on top of TensorFlow or Theano. We know
already how to install TensorFlow using pip.
« pip install theano «
Theano
Theano is an open source deep learning library that allows you to
evaluate multi-dimensional arrays effectively. We can easily install
15. D. OVERVIEW OF DEEP LEARNING
• We can define like that Deep Learning is consisting of layers
which are follows each other to get useful features.
• Deep learning is sub-branch of machine learning.
• ‘Deep’ means that it does not mean that get deeeeeep magicful
knowledge.This word means that multi-layers and amount of
layers point out the depth of a model.
• Modern deep learning algortithms have thousunds layers.
• This layered notation is called ‘ Neural Networks ‘ which is
inspired neurobiology but , we can say that this model
process does not exactly match with neuro-science even some
articles show opposite idea.
16. D. OVERVIEW OF
DEEP LEARNING
In deep learning, a computer
model learns to perform
classification tasks directly from
images, text, or sound. Deep
learning models can achieve
state-of-the-art accuracy,
sometimes exceeding human-
level performance. Models are
trained by using a large set of
labeled data and neural network
architectures that contain many
layers..On the right side , you
can see the single perception of
deep learning.
17. D. OVERVIEW OF
DEEP LEARNING
Multi-Layer perceptron is the
simplest form of ANN. It consists
of a single input layer, one or
more hidden layer and finally an
output layer. A layer consists of
a collection of perceptron. Input
layer is basically one or more
features of the input data. Every
hidden layer consists of one or
more neurons and process
certain aspect of the feature and
send the processed information
into the next hidden layer. The
output layer process receives the
data from last hidden layer and
18. D. OVERVIEW OF DEEP LEARNING
General pipeline for implementing an ANN (Artifical NEURAL
NETWORKS):
We can simply define the procedure like that ;
• DECIDE WHICH DEEP LEARNING ARCITECHTURE YOU NEED (CNN ?
,RNN ? ) !!!
• Firstly we need labeled data ( as much as big data).And do not worry
about how your code understand which part of data is necessary for
your train., Keras handle it.
• After defining how we need input and output data shape.( datas
should be seperated as x_train, y_train , x_test , y_train).
• Train and test datas should be different each other.
• Design a architecture model to catch maximum accurucy.(Do not
forget our goal is reaching max accurucy without overfitting)
• Choose the correct optimizers ,actication function ,loss function.
19. E. DEEP LEARNING WITH KERAS
Implementing a neural network in Keras :
• Preparing the input and specify the input dimension (size)
• •Define the model architecture and build the computational
graph
• •Specify the optimizer and configure the learning process
• •Specify the Inputs, Outputs of the computational graph
(model) and the Loss function
• Train and test the model on the dataset
20. E. DEEP LEARNING WITH KERAS
• In Keras, every ANN is represented by Keras Models. In turn, every
Keras Model is composition of Keras Layers and represents ANN layers
like input, hidden layer, output layers, convolution layer, pooling layer,
etc., Keras model and layer access Keras modules for activation
function, loss function, regularization function, etc., Using Keras
model, Keras Layer, and Keras modules, any ANN algorithm (CNN, RNN,
etc.,) can be represented in a simple and efficient manner. The
following diagram depicts the relationship between model, layer and
core modules:
21. F. MODULE
Keras modules cosist of classes, functions and variables.These are pre-defined,The modules make simpler
to build model.
List of Modules :
• Initializers
• Constrains
• Activations
• Utilities
• Backend:
• Sequence processing
• Image processing
• Text processing
• Optimizers
• Callback
• Metrics
• Losses
22. G.LAYERS
• As I pointed out , Keras layers are the essentials for the Keras
models. Each layer takes input what previous layer provide as
output, and this circle keeps on the untill last layer.
• A layer needs to know shape of the input to get structue of the
input data.
• Layers also needs to know number of neurons , initializers,
regularizers, constraints and activation function.
23. G.LAYERS
Let us create a simple Keras layer using Sequential model API to get the
idea of how Keras model ;
EXAMPLE - 1 :
NOTE : Google Colaboratory is
used to execute.Google gives
us free Cpu and Gpu.
25. G.LAYERS
Activations
• The activation function makes input data non linear , and model
learn better.
• The output of a perceptron (neuron) is simply the result of the
activation function, which accepts the summation of all input
multiplied with its corresponding weight plus overall bias. (result
= Activation(SUMOF(input * weight) + bias)
• Activation Function in the keras module = linear , elu , selu , relu
, softmax , softplus , softsign , tanh , sigmoid , hard_sigmoid ,
exponential .
26. G.LAYERS
Dense Layer
• Dense layer does the below operation on the input and return the
output.
• output = activation(dot(input, kernel) + bias)
EXAMPLE 2- Lets find a result with using input and kernel :
Let us consider sample input and weights as below and try to find
the result:
• input as 2 x 2 matrix [ [1, 2], [3, 4] ]
• kernel as 2 x 2 matrix [ [0.5, 0.75], [0.25, 0.5] ]
• bias value as 0
28. G.LAYERS
Dropout Layers
Dropout layer is used generally in most of models, to prevent
overfitting.Dropout layer deletes a noise which node has . We can
simply define that when we train the model, yes we want to teach
to model essential things to predict correctly. But ıf models
memorize the labeled input data ,the model works on only what
model see before. The model should work on not even see before
test data. Memorizing and overfitting are similar things.
29. G.LAYERS
• Dropout has three arguments and they are as follows:
« keras.layers.Dropout(rate, noise_shape=None, seed=None) «
rate represent the fraction of the input unit to be dropped. It will
be from 0 to 1.
• noise_shape represent the dimension of the shape in which the
dropout to be applied. For example, the input shape is
(batch_size, timesteps, features). Then, to apply dropout in the
timesteps, (batch_size, 1, features) need to be specified as
noise_shape
• seed - random seed.
30. G.LAYERS
Flatten Layers
• Flatten layers also are used to get results together on the one
row or one column .Most of time have been on before last layer.
• For example, if flatten is applied to layer having input shape as
(batch_size, 2,2), then the output shape of the layer will be
(batch_size, 4) .
• « keras.layers.Flatten(data_format=None) «
32. H.MODEL COMPILATION
• Now, how to compile the model will be explained .
• The compilation is the final part of building a model.
Loss = Loss function is used to find error or deviation in the
learning process. Keras requires loss function during model
compilation process. Keras have pre-defined some loss
functions. We chooce one of these depends on our desire.
33. H.MODEL COMPILATION
Optimizer = Optimization is a vital , when training. This is for
optimizing the input weights by comparing the prediction and
loss function results.
For example ; Stochastic gradient descent optimizer , Adam
optimizer , Adamax optimizer ..
Metrics = Always we try to get better and better result. To do
this , we need to observe the performance of model.
Metrics is used to evaluate the performance , it is similar to loss
function, but not used in training process.
• For example ; accuracy , categorical_accuracy ..
34. H.MODEL COMPILATION
• Keras model provides a method, compile() to compile the
model.
• The argument and default value of the compile() method is as
follows:
« compile(optimizer, loss=None, metrics=None,
loss_weights=None, sample_weight_mode=None,
weighted_metrics=None, target_tensors=None) »
35. H.MODEL COMPILATION
Model Training
Models are trained by NumPy arrays using fit(). The main
purpose of this fit function is used to evaluate your model on
training. This can be also used for graphing model performance .
« model.fit(X, y, epochs=, batch_size=) »
• X, y - It is a tuple to evaluate your data.
• epochs - no of times the model is needed to be evaluated
during training.
• batch_size - training instances
38. H.MODEL COMPILATION
Example 5 : Let us choose a simple multi-layer perceptron (MLP) as represented
below and try to create the model using Keras ;
The core features of the model are as follows:
• Input layer consists of 784 values (28 x 28 = 784).
• First hidden layer, Dense consists of 512 neurons and ‘relu’ activation function.
• Second hidden layer, Dropout has 0.2 as its value.
• Third hidden layer, again Dense consists of 512 neurons and ‘relu’ activation
function.
• Fourth hidden layer, Dropout has 0.2 as its value.
• Fifth and final layer consists of 10 neurons and ‘softmax’ activation function.
• Use categorical_crossentropy as loss function.
• Use RMSprop() as Optimizer
• Use accuracy as metrics.
• Use 128 as batch size.
42. I. CONVOLUTIONAL NEURAL NETWORK
(CNN)
• The difference between CNN and ANN is that ANN is trained by
global image , but CNN learns partially for example; if there is
a image which is 10x10 , with cnn model learn 2 x2 , 2x2 ,
2x2.. Its similar to signal filtering.
• As you predict that there will be more details , and it causes
better accuracy.
• CNN is used most of time for images.
43. I. CONVOLUTIONAL NEURAL NETWORK
(CNN)
We will observe CNN with a example which is like ‘ hello world ‘ of
deep learning .
We will use mnist dataset that consists of 70,000 images
handwritten digits from 0 to 9 . Lets do the example to idetify them
using a CNN.
Example 6 : Mini-project idetify handwritten number
49. J. LSTM – RNN
• RNNs can use their internal state (memory) to process sequences of
inputs. This makes them applicable to tasks such as unsegmented,
connected handwriting recognition or speech recognition. In other
neural networks, all the inputs are independent of each other. But in
RNN, all the inputs are related to each other.
Advantages of Recurrent Neural Network
• RNN can model sequence of data so that each sample can be
assumed to be dependent on previous ones
• Recurrent neural network are even used with convolutional layers to
extend the effective pixel neighbourhood.
Disadvantages of Recurrent Neural Network
• Gradient vanishing and exploding problems.
• Training an RNN is a very difficult task.
• It cannot process very long sequences if using tanh or relu as an
activation function.
50. J. LSTM – RNN
• Long Short-Term Memory (LSTM) networks are a modified
version of recurrent neural networks, which makes it easier to
remember past data in memory.
• LSTM is well-suited to classify, process and predict time series
given time lags of unknown duration. It trains the model by
using back-propagation.
• Lets see this part with an example which is that while
predicting the actual price of a stock is an uphill climb, we can
build a model that will predict whether the price will go up or
down. The data and notebook used for this tutorial can be
found (https://github.com/mwitiderrick/stockprice). It’s
important to note that there are always other factors that affect
57. K.APPLICATIONS
As ı said before; more data , more accuracy. But if you have small
dataset for images . What will you do ?! There is a solution which
is Data Augmentation. That means that we play game with
images like ;symetry , rotation , zoom …
We need to just use ImageDataGenerator module. Lets see with
one example ...
Example -8 : We have 1 image and we want to much more
data.Here is our images that is by Ara Guler :
69. CONCLUSION
To sum up , the tutorial shows us that there are so many
parameters to adjuct on your model, this skill will be gained by
project experience . We can think like that we are in labaratory ,
we are doing experiment as building our deep learning model. I
gained a experience also about learning and teaching when
preparing the tutorial.In addition , I saw my weakness on some
points which I will fix these as soon as possible. Finally, after
working on this tutorial with much more detail, I will upload on my
github account.
70. REFERENCES
• Deep Learning with Python-François Chollet-Manning
Publications Company, 2017
• Keras.io
• https://www.kdnuggets.com/
• https://www.mathworks.com/
• medium.com