This document provides an overview and introduction to TensorFlow 2. It discusses major changes from TensorFlow 1.x like eager execution and tf.function decorator. It covers working with tensors, arrays, datasets, and loops in TensorFlow 2. It also demonstrates common operations like arithmetic, reshaping and normalization. Finally, it briefly introduces working with Keras and neural networks in TensorFlow 2.
A fast-paced introduction to TensorFlow 2 about some important new features (such as generators and the @tf.function decorator) and TF 1.x functionality that's been removed from TF 2 (yes, tf.Session() has retired).
Some concise code samples are presented to illustrate how to use new features of TensorFlow 2.
A fast-paced introduction to TensorFlow 2 about some important new features (such as generators and the @tf.function decorator) and TF 1.x functionality that's been removed from TF 2 (yes, tf.Session() has retired).
Concise code samples are presented to illustrate how to use new features of TensorFlow 2. You'll also get a quick introduction to lazy operators (if you know FRP this will be super easy), along with a code comparison between TF 1.x/iterators with tf.data.Dataset and TF 2/generators with tf.data.Dataset.
Finally, we'll look at some tf.keras code samples that are based on TensorFlow 2. Although familiarity with TF 1.x is helpful, newcomers with an avid interest in learning about TensorFlow 2 can benefit from this session.
This session for beginners introduces tf.data APIs for creating data pipelines by combining various "lazy operators" in tf.data, such as filter(), map(), batch(), zip(), flatmap(), take(), and so forth.
Familiarity with method chaining and TF2 is helpful (but not required). If you are comfortable with FRP, the code samples in this session will be very familiar to you.
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs, followed by a Keras code sample for defining a CNN. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session. Then we'll see a short introduction to TensorFlow 1.x and some insights into TF 2 that will be released some time this year.
Introduction to Deep Learning, Keras, and TensorflowOswald Campesato
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. Then we'll see how to create a Convolutional Neural Network in Keras, followed by a quick introduction to TensorFlow and TensorBoard.
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, followed by some TensorFlow features, and then a code sample of training a CNN in tensorflow.js. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
Abstract: This PDSG workshop introduces basic concepts on TensorFlow. The course covers fundamentals. Concepts covered are Vectors/Matrices/Vectors, Design&Run, Constants, Operations, Placeholders, Bindings, Operators, Loss Function and Training.
Level: Fundamental
Requirements: Some basic programming knowledge is preferred. No prior statistics background is required.
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. Then we'll see a short introduction to TensorFlow and TensorBoard.
A fast-paced introduction to TensorFlow 2 about some important new features (such as generators and the @tf.function decorator) and TF 1.x functionality that's been removed from TF 2 (yes, tf.Session() has retired).
Some concise code samples are presented to illustrate how to use new features of TensorFlow 2.
A fast-paced introduction to TensorFlow 2 about some important new features (such as generators and the @tf.function decorator) and TF 1.x functionality that's been removed from TF 2 (yes, tf.Session() has retired).
Concise code samples are presented to illustrate how to use new features of TensorFlow 2. You'll also get a quick introduction to lazy operators (if you know FRP this will be super easy), along with a code comparison between TF 1.x/iterators with tf.data.Dataset and TF 2/generators with tf.data.Dataset.
Finally, we'll look at some tf.keras code samples that are based on TensorFlow 2. Although familiarity with TF 1.x is helpful, newcomers with an avid interest in learning about TensorFlow 2 can benefit from this session.
This session for beginners introduces tf.data APIs for creating data pipelines by combining various "lazy operators" in tf.data, such as filter(), map(), batch(), zip(), flatmap(), take(), and so forth.
Familiarity with method chaining and TF2 is helpful (but not required). If you are comfortable with FRP, the code samples in this session will be very familiar to you.
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs, followed by a Keras code sample for defining a CNN. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session. Then we'll see a short introduction to TensorFlow 1.x and some insights into TF 2 that will be released some time this year.
Introduction to Deep Learning, Keras, and TensorflowOswald Campesato
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. Then we'll see how to create a Convolutional Neural Network in Keras, followed by a quick introduction to TensorFlow and TensorBoard.
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, followed by some TensorFlow features, and then a code sample of training a CNN in tensorflow.js. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
Abstract: This PDSG workshop introduces basic concepts on TensorFlow. The course covers fundamentals. Concepts covered are Vectors/Matrices/Vectors, Design&Run, Constants, Operations, Placeholders, Bindings, Operators, Loss Function and Training.
Level: Fundamental
Requirements: Some basic programming knowledge is preferred. No prior statistics background is required.
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. Then we'll see a short introduction to TensorFlow and TensorBoard.
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.
Workshop about TensorFlow usage for AI Ukraine 2016. Brief tutorial with source code example. Described TensorFlow main ideas, terms, parameters. Example related with linear neuron model and learning using Adam optimization algorithm.
A fast-paced introduction to Deep Learning (DL) concepts, starting with a simple yet complete neural network (no frameworks), followed by aspects of deep neural networks, such as back propagation, activation functions, CNNs, and the AUT theorem. Next, a quick introduction to TensorFlow and TensorBoard, along with some code samples with TensorFlow. For best results, familiarity with basic vectors and matrices, inner (aka "dot") products of vectors, the notion of a derivative, and rudimentary Python is recommended.
Intro to Deep Learning, TensorFlow, and tensorflow.jsOswald Campesato
This fast-paced session introduces Deep Learning concepts, such gradient descent, back propagation, activation functions, and CNNs. We'll look at creating Android apps with TensorFlow Lite (pending its availability). Basic knowledge of vectors, matrices, and Android, as well as elementary calculus (derivatives), are strongly recommended in order to derive the maximum benefit from this session.
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, followed by some TensorFlow features, and then a code sample of training a CNN in tensorflow.js. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
Introduction To TensorFlow | Deep Learning Using TensorFlow | CloudxLabCloudxLab
( Machine Learning & Deep Learning Specialization Training: https://goo.gl/6n3vko )
This CloudxLab TensorFlow tutorial helps you to understand TensorFlow in detail. Below are the topics covered in this tutorial:
1) Why TensorFlow?
2) What are Tensors?
3) What is TensorFlow?
4) Creating your First Graph
5) Linear Regression with TensorFlow
6) Implementing Gradient Descent using TensorFlow
7) Implementing Gradient Descent Using autodiff
8) Implementing Gradient Descent Using an Optimizer
9) Graph Visualization using TensorBoard
10) Name Scopes in TensorFlow
11) Modularity in TensorFlow
12) Sharing Variables in TensorFlow
Introduction to TensorFlow, by Machine Learning at BerkeleyTed Xiao
A workshop introducing the TensorFlow Machine Learning framework. Presented by Brenton Chu, Vice President of Machine Learning at Berkeley.
This presentation cover show to construct, train, evaluate, and visualize neural networks in TensorFlow 1.0
http://ml.berkeley.edu
An introduction to Google's AI Engine, look deeper into Artificial Networks and Machine Learning. Appreciate how our simplest neural network be codified and be used to data analytics.
"A fast-paced introduction to Deep Learning (DL) concepts, such as neural networks, back propagation, activation functions, and CNNs. We'll also look at JavaScript-based toolkits (such as TensorFire and deeplearning.js) that leverage the power of WebGL. Basic knowledge of elementary calculus (e.g., derivatives) is recommended in order to derive the maximum benefit from this session.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/dec-2019-alliance-vitf-facebook
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Joseph Spisak, Product Manager at Facebook, delivers the presentation "PyTorch Deep Learning Framework: Status and Directions" at the Embedded Vision Alliance's December 2019 Vision Industry and Technology Forum. Spisak gives an update on the Torch deep learning framework and where it’s heading.
TensorFlow Tutorial | Deep Learning Using TensorFlow | TensorFlow Tutorial Py...Edureka!
This Edureka TensorFlow Tutorial (Blog: https://goo.gl/HTE7uB) will help you in understanding various important basics of TensorFlow. It also includes a use-case in which we will create a model that will differentiate between a rock and a mine using TensorFlow. Below are the topics covered in this tutorial:
1. What are Tensors?
2. What is TensorFlow?
3. TensorFlow Code-basics
4. Graph Visualization
5. TensorFlow Data structures
6. Use-Case Naval Mine Identifier (NMI)
Explanation on Tensorflow example -Deep mnist for expert홍배 김
you can find the exact and detailed network architecture of 'Deep mnist for expert' example of tensorflow's tutorial. I also added descriptions on the program for your better understanding.
Introduction To TensorFlow | Deep Learning with TensorFlow | TensorFlow For B...Edureka!
** AI & Deep Learning with Tensorflow Training: https://goo.gl/vDxgi5 **
This Edureka tutorial on "Introduction to TensorFlow" provides you an insight into one of the top Deep Learning frameworks that you should consider learning!
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
This fast-paced session starts with an introduction to neural networks and linear regression models, along with a quick view of TensorFlow, followed by some Scala APIs for TensorFlow. You'll also see a simple dockerized image of Scala and TensorFlow code and how to execute the code in that image from the command line. No prior knowledge of NNs, Keras, or TensorFlow is required (but you must be comfortable with Scala).
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.
Workshop about TensorFlow usage for AI Ukraine 2016. Brief tutorial with source code example. Described TensorFlow main ideas, terms, parameters. Example related with linear neuron model and learning using Adam optimization algorithm.
A fast-paced introduction to Deep Learning (DL) concepts, starting with a simple yet complete neural network (no frameworks), followed by aspects of deep neural networks, such as back propagation, activation functions, CNNs, and the AUT theorem. Next, a quick introduction to TensorFlow and TensorBoard, along with some code samples with TensorFlow. For best results, familiarity with basic vectors and matrices, inner (aka "dot") products of vectors, the notion of a derivative, and rudimentary Python is recommended.
Intro to Deep Learning, TensorFlow, and tensorflow.jsOswald Campesato
This fast-paced session introduces Deep Learning concepts, such gradient descent, back propagation, activation functions, and CNNs. We'll look at creating Android apps with TensorFlow Lite (pending its availability). Basic knowledge of vectors, matrices, and Android, as well as elementary calculus (derivatives), are strongly recommended in order to derive the maximum benefit from this session.
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, followed by some TensorFlow features, and then a code sample of training a CNN in tensorflow.js. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.
Introduction To TensorFlow | Deep Learning Using TensorFlow | CloudxLabCloudxLab
( Machine Learning & Deep Learning Specialization Training: https://goo.gl/6n3vko )
This CloudxLab TensorFlow tutorial helps you to understand TensorFlow in detail. Below are the topics covered in this tutorial:
1) Why TensorFlow?
2) What are Tensors?
3) What is TensorFlow?
4) Creating your First Graph
5) Linear Regression with TensorFlow
6) Implementing Gradient Descent using TensorFlow
7) Implementing Gradient Descent Using autodiff
8) Implementing Gradient Descent Using an Optimizer
9) Graph Visualization using TensorBoard
10) Name Scopes in TensorFlow
11) Modularity in TensorFlow
12) Sharing Variables in TensorFlow
Introduction to TensorFlow, by Machine Learning at BerkeleyTed Xiao
A workshop introducing the TensorFlow Machine Learning framework. Presented by Brenton Chu, Vice President of Machine Learning at Berkeley.
This presentation cover show to construct, train, evaluate, and visualize neural networks in TensorFlow 1.0
http://ml.berkeley.edu
An introduction to Google's AI Engine, look deeper into Artificial Networks and Machine Learning. Appreciate how our simplest neural network be codified and be used to data analytics.
"A fast-paced introduction to Deep Learning (DL) concepts, such as neural networks, back propagation, activation functions, and CNNs. We'll also look at JavaScript-based toolkits (such as TensorFire and deeplearning.js) that leverage the power of WebGL. Basic knowledge of elementary calculus (e.g., derivatives) is recommended in order to derive the maximum benefit from this session.
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/dec-2019-alliance-vitf-facebook
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Joseph Spisak, Product Manager at Facebook, delivers the presentation "PyTorch Deep Learning Framework: Status and Directions" at the Embedded Vision Alliance's December 2019 Vision Industry and Technology Forum. Spisak gives an update on the Torch deep learning framework and where it’s heading.
TensorFlow Tutorial | Deep Learning Using TensorFlow | TensorFlow Tutorial Py...Edureka!
This Edureka TensorFlow Tutorial (Blog: https://goo.gl/HTE7uB) will help you in understanding various important basics of TensorFlow. It also includes a use-case in which we will create a model that will differentiate between a rock and a mine using TensorFlow. Below are the topics covered in this tutorial:
1. What are Tensors?
2. What is TensorFlow?
3. TensorFlow Code-basics
4. Graph Visualization
5. TensorFlow Data structures
6. Use-Case Naval Mine Identifier (NMI)
Explanation on Tensorflow example -Deep mnist for expert홍배 김
you can find the exact and detailed network architecture of 'Deep mnist for expert' example of tensorflow's tutorial. I also added descriptions on the program for your better understanding.
Introduction To TensorFlow | Deep Learning with TensorFlow | TensorFlow For B...Edureka!
** AI & Deep Learning with Tensorflow Training: https://goo.gl/vDxgi5 **
This Edureka tutorial on "Introduction to TensorFlow" provides you an insight into one of the top Deep Learning frameworks that you should consider learning!
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
This fast-paced session starts with an introduction to neural networks and linear regression models, along with a quick view of TensorFlow, followed by some Scala APIs for TensorFlow. You'll also see a simple dockerized image of Scala and TensorFlow code and how to execute the code in that image from the command line. No prior knowledge of NNs, Keras, or TensorFlow is required (but you must be comfortable with Scala).
Introduction to Deep Learning, Keras, and TensorFlowSri Ambati
This meetup was recorded in San Francisco on Jan 9, 2019.
Video recording of the session can be viewed here: https://youtu.be/yG1UJEzpJ64
Description:
This fast-paced session starts with a simple yet complete neural network (no frameworks), followed by an overview of activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Next, we'll create a neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. For best results, familiarity with basic vectors and matrices, inner (aka "dot") products of vectors, and rudimentary Python is definitely helpful. If time permits, we'll look at the UAT, CLT, and the Fixed Point Theorem. (Bonus points if you know Zorn's Lemma, the Well-Ordering Theorem, and the Axiom of Choice.)
Oswald's Bio:
Oswald Campesato is an education junkie: a former Ph.D. Candidate in Mathematics (ABD), with multiple Master's and 2 Bachelor's degrees. In a previous career, he worked in South America, Italy, and the French Riviera, which enabled him to travel to 70 countries throughout the world.
He has worked in American and Japanese corporations and start-ups, as C/C++ and Java developer to CTO. He works in the web and mobile space, conducts training sessions in Android, Java, Angular 2, and ReactJS, and he writes graphics code for fun. He's comfortable in four languages and aspires to become proficient in Japanese, ideally sometime in the next two decades. He enjoys collaborating with people who share his passion for learning the latest cool stuff, and he's currently working on his 15th book, which is about Angular 2.
Boosting machine learning workflow with TensorFlow 2.0Jeongkyu Shin
TensorFlow 2.0 is the latest release aimed at user convenience, API simplicity, and scalability across multiple platforms. In addition, TensorFlow 2.0, along with a variety of new projects in the TensorFlow ecosystem, TFX, TF-Agent, and TF federated, can help you quickly and easily create a wide variety of machine learning models in more environments. This talk will introduce TensorFlow 2.0 and discusses how to develop and optimize machine learning workflows based on TensorFlow 2.0 and projects within the various TensorFlow ecosystems.
This slide was presented at GDG DevFest Songdo on November 30, 2019.
Title
Hands-on Learning with KubeFlow + Keras/TensorFlow 2.0 + TF Extended (TFX) + Kubernetes + PyTorch + XGBoost + Airflow + MLflow + Spark + Jupyter + TPU
Video
https://youtu.be/vaB4IM6ySD0
Description
In this workshop, we build real-world machine learning pipelines using TensorFlow Extended (TFX), KubeFlow, and Airflow.
Described in the 2017 paper, TFX is used internally by thousands of Google data scientists and engineers across every major product line within Google.
KubeFlow is a modern, end-to-end pipeline orchestration framework that embraces the latest AI best practices including hyper-parameter tuning, distributed model training, and model tracking.
Airflow is the most-widely used pipeline orchestration framework in machine learning.
Pre-requisites
Modern browser - and that's it!
Every attendee will receive a cloud instance
Nothing will be installed on your local laptop
Everything can be downloaded at the end of the workshop
Location
Online Workshop
Agenda
1. Create a Kubernetes cluster
2. Install KubeFlow, Airflow, TFX, and Jupyter
3. Setup ML Training Pipelines with KubeFlow and Airflow
4. Transform Data with TFX Transform
5. Validate Training Data with TFX Data Validation
6. Train Models with Jupyter, Keras/TensorFlow 2.0, PyTorch, XGBoost, and KubeFlow
7. Run a Notebook Directly on Kubernetes Cluster with KubeFlow
8. Analyze Models using TFX Model Analysis and Jupyter
9. Perform Hyper-Parameter Tuning with KubeFlow
10. Select the Best Model using KubeFlow Experiment Tracking
11. Reproduce Model Training with TFX Metadata Store and Pachyderm
12. Deploy the Model to Production with TensorFlow Serving and Istio
13. Save and Download your Workspace
Key Takeaways
Attendees will gain experience training, analyzing, and serving real-world Keras/TensorFlow 2.0 models in production using model frameworks and open-source tools.
Related Links
1. PipelineAI Home: https://pipeline.ai
2. PipelineAI Community Edition: http://community.pipeline.ai
3. PipelineAI GitHub: https://github.com/PipelineAI/pipeline
4. Advanced Spark and TensorFlow Meetup (SF-based, Global Reach): https://www.meetup.com/Advanced-Spark-and-TensorFlow-Meetup
5. YouTube Videos: https://youtube.pipeline.ai
6. SlideShare Presentations: https://slideshare.pipeline.ai
7. Slack Support: https://joinslack.pipeline.ai
8. Web Support and Knowledge Base: https://support.pipeline.ai
9. Email Support: support@pipeline.ai
TF2.0 is designed to improve usability and productivity. As a TF's enthusiastic user, I am very excited. Personally, I think the most important thing about usability is "how does TF provide a user-friendly API?" Aside from the other aspects in TF 2.0, this post was a quick review from an API usage perspective.
Paolo Galeone - Dissecting tf.function to discover auto graph strengths and s...MeetupDataScienceRoma
Original presentation available on GitHub: https://pgaleone.eu/tf-function-talk/
Meetup: https://www.meetup.com/it-IT/Machine-Learning-Data-Science-Meetup/events/264338606/
YouTube Link: https://youtu.be/SpZSMvI-keU
** AI & Deep Learning with Tensorflow Training: https://www.edureka.co/ai-deep-learning-with-tensorflow **
This Edureka PPT will provide you with a crisp comparison between the two Deep Learning Frameworks - Theano and TensorFlow and will help you choose the right one for yourself.
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
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
Castbox: https://castbox.fm/networks/505?country=in
Real Time Streaming Data with Kafka and TensorFlow (Yong Tang, MobileIron) Ka...confluent
In mission-critical real time applications, using machine learning to analyze streaming data are gaining momentum. In those applications Apache Kafka is the most widely used framework to process the data streams. It typically works with other machine learning frameworks for model inference and training purposes. In this talk, our focus is to discuss the KafkaDataset module in TensorFlow. KafkaDataset processes Kafka streaming data directly to TensorFlow’s graph. As a part of Tensorflow (in ‘tf.contrib’), the implementation of KafkaDataset is mostly written in C++. The module exposes a machine learning friendly Python interface through Tensorflow’s ‘tf.data’ API. It could be directly feed to ‘tf.keras’ and other TensorFlow modules for training and inferencing purposes. Combined with Kafka streaming itself, the KafkaDataset module in TensorFlow removes the need to have an intermediate data processing infrastructure. This helps many mission-critical real time applications to adopt machine learning more easily. At the end of the talk we will walk through a concrete example with a demo to showcase the usage we described.
Lucio Floretta - TensorFlow and Deep Learning without a PhD - Codemotion Mila...Codemotion
With TensorFlow, deep machine learning transitions from an area of research to mainstream software engineering. In this session, we'll work together to construct and train a neural network that recognises handwritten digits. Along the way, we'll discover some of the "tricks of the trade" used in neural network design, and finally, we'll bring the recognition accuracy of our model above 99%.
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.
This fast-paced session provides a brief history of AI, followed by AI-related topics, such as Machine Learning, Deep Learning and Reinforcement Learning, and the most popular frameworks for Machine Learning. You will learn about some of the successes of AI, and also some of the significant challenges in AI. No specialized knowledge is required, but an avid interest is recommended to derive the maximum benefit from this session.
Introduction to Deep Learning for Non-ProgrammersOswald Campesato
This session provides a brief history of AI, followed by AI-related topics, such as robots in AI, Machine Learning and Deep Learning, use cases for AI, some of the successes of AI, and also some of the significant challenges in AI. You will also learn about AI and mobile devices and the ethics of AI. An avid interest is recommended to derive the maximum benefit from this session.
This slide deck introduces Deep Learning concepts, such gradient descent, back propagation, activation functions, and CNNs. Basic knowledge of vectors, matrices, and Android, as well as elementary calculus (derivatives), are strongly recommended in order to derive the maximum benefit from this session.
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and elementary calculus (derivatives), are helpful in order to derive the maximum benefit from this session.
Next we'll see a simple neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. (Bonus points if you know Zorn's Lemma, the Well-Ordering Theorem, and the Axiom of Choice.)
A fast-paced introduction to Deep Learning that starts with a simple yet complete neural network (no frameworks), followed by an overview of activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Next we'll create a neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. For best results, familiarity with basic vectors and matrices, inner (aka "dot") products of vectors, and rudimentary Python is definitely helpful.
A fast-paced introduction to Deep Learning (DL) concepts, such as neural networks, back propagation, activation functions, CNNs, RNNs (if time permits), and the CLT/AUT/fixed-point theorems, along with a basic code sample in TensorFlow.
During this session you will learn how to manually create a basic neural network that acts as a classifier, and also the segue from linear regression to a neural network.
You'll also learn about GANs (Generative Adversarial Networks) for static images as well as voice, and the former case, their potential impact on self-driving cars.
An introduction to Deep Learning (DL) concepts, starting with a simple yet complete neural network (no frameworks), followed by aspects of deep neural networks, such as back propagation, activation functions, CNNs, and the AUT theorem. Next, a quick introduction to TensorFlow and Tensorboard, and then some code samples with Scala and TensorFlow.
An introduction to Deep Learning (DL) concepts, such as neural networks, back propagation, activation functions, CNNs, and GANs, along with a simple yet complete neural network.
An introduction to Deep Learning concepts, with a simple yet complete neural network, CNNs, followed by rudimentary concepts of Keras and TensorFlow, and some simple code fragments.
An introduction to Deep Learning (DL) concepts, such as neural networks, back propagation, activation functions, CNNs, RNNs (if time permits), and the CLT/AUT/fixed-point theorems, along with code samples in Java and TensorFlow.
This presentation focuses on Deep Learning (DL) concepts, such as neural networks, backprop, activation functions, and Convolutional Neural Networks. You'll also learn how to incorporate Deep Learning in Android applications. Basic knowledge of matrices is helpful for this session, which is targeted primarily to beginners.
An introduction to Kotlin for advanced Android beginners, covering command-line compilation of Kotlin files, conditional logic, val/var, basic functions, higher order functions, recursion.
This presentation focuses on Deep Learning (DL) concepts, such as neural networks, backprop, activation functions, and Convolutional Neural Networks, followed by a TypeScript-based code sample that replicates the Tensorflow playground. Basic knowledge of matrices is helpful for this session.
This presentation focuses on Deep Learning (DL) concepts, such as neural neworks, backprop, activation functions, and Convolutional Neural Networks, with a short introduction to D3, and followed by a TypeScript-based code sample that replicates the TensorFlow playground. Basic knowledge of matrices is helpful.
This presentation focuses on Deep Learning (DL) concepts, such as neural neworks, backprop, activation functions, and Convolutional Neural Networks, with a short introduction to D3, and followed by a TypeScript-based code sample that replicates the TensorFlow playground. Basic knowledge of matrices is helpful.
This presentation introduces Deep Learning (DL) concepts, such as neural neworks, backprop, activation functions, and Convolutional Neural Networks, followed by an Angular application that uses TypeScript in order to replicate the Tensorflow playground.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
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In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
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And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
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Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
The Art of the Pitch: WordPress Relationships and Sales
Introduction to TensorFlow 2 and Keras
1. Introduction to TensorFlow 2
GDG Meetup SF
Google Launchpad
11/19/2019
Oswald Campesato
ocampesato@yahoo.com
2. Highlights/Overview
What is TensorFlow 2?
Major Changes in TF 2
Working with strings/arrays/tensors
Working with TF 2 @tf.function decorator
Working with TF 2 generators
Working with TF 2 tf.data.Dataset
Datasets in TF 1.x versus TF 2
Working with TF 2 tf.keras
CNNs, RNNs, LSTMs, Bidirectional LSTMs
Reinforcement Learning/TF Agents
3. Highlights/Overview
This session does not cover:
“the new vision” of TF 2
“the common practices” of TF 2
“lessons learned” from TF 1.x
a detailed “roadmap” from TF 1.x to TF 2
4. What is TensorFlow 2?
An open source framework for ML and DL
Created by Google (TF 1.x released 11/2015)
Evolved from Google Brain
Linux and Mac OS X support (VM for Windows)
Production release: 10/2019
TF home page: https://www.tensorflow.org/
5. TF 2 Platform Support
Tested and supported on 64-bit systems
Ubuntu 16.04 or later
Windows 7 or later
macOS 10.12.6 (Sierra) or later (no GPU support)
Raspbian 9.0 or later
6. TF 2 Docker Images
https://hub.docker.com/r/tensorflow/tensorflow/
$ docker run -it --rm tensorflow/tensorflow bash
Start a CPU-only container with Python 2:
$ docker run -it --rm --runtime=nvidia tensorflow/tensorflow:latest-
gpu-py3 python
Start a GPU container with Python 3 & Python interpreter:
$ docker run -it --rm -v $(realpath ~/notebooks):/tf/notebooks -p
8888:8888 tensorflow/tensorflow:late
7. What is TensorFlow 2?
ML/DL open source platform
Support for Python, Java, C++
Desktop, Server, Mobile, Web
CPU/GPU/TPU support
Visualization via TensorBoard
Can be embedded in Python scripts
Installation: pip install tensorflow
Ex: pip install tensorflow==2.0.0-alpha0
9. Major Changes in TF 2
TF 2 Eager Execution: default mode
@tf.function decorator: instead of tf.Session()
AutoGraph: graph generated in @tf.function()
Generators: used with tf.data.Dataset (TF 2)
Differentiation: calculates gradients
tf.GradientTape: automatic differentiation
10. Removed from TensorFlow 2
tf.Session()
tf.placeholder()
tf.global_initializer()
feed_dict
Variable scopes
tf.contrib code
tf.flags and tf.contrib
Global variables
=> TF 1.x functions moved to compat.v1
11. Upgrading Files/Directories to TF 2
The TF 2 upgrade script:
https://www.tensorflow.org/alpha/guide/upgrade
Script name: tf_upgrade_v2
install TF to get the upgrade script:
pip install tensorflow (NOT pip3)
12. Upgrading Files/Directories to TF 2
1) upgrade one file:
tf_upgrade_v2 --infile oldtf.py --outfile newtf.py
The preceding command creates report.txt
Upgrade an entire directory:
tf_upgrade_v2 –intree mydir --outtree newtf.py –
copyotherfiles False
NB: do NOT make manual upgrade changes
13. Migrating to TensorFlow 2
1) do not manually update parts of your code
2) script won't work with functions with reordered arguments
3) script assumes this TF statement: "import tensorflow as tf"
4) the script does not reorder arguments
5) the script adds keyword arguments to functions that have their
arguments reordered.
Upgrade Jupyter notebooks and Python files in a GitHub repo:
http://tf2up.ml/
=> Replace prefix https://github.com/ with http://tf2up.m
14. What is a Tensor?
TF tensors are n-dimensional arrays
TF tensors are very similar to numpy ndarrays
scalar number: a zeroth-order tensor
vector: a first-order tensor
matrix: a second-order tensor
3-dimensional array: a 3rd order tensor
https://dzone.com/articles/tensorflow-simplified-
examples
16. TensorFlow Eager Execution
integration with Python tools
Supports dynamic models + Python control flow
support for custom and higher-order gradients
=> Default mode in TensorFlow 2.0
17. TensorFlow Eager Execution
import tensorflow as tf
x = [[2.]]
# m = tf.matmul(x, x) <= deprecated
m = tf.linalg.matmul(x, x)
print("m:",m)
print("m:",m.numpy())
#Output:
#m: tf.Tensor([[4.]], shape=(1,1),dtype=float32)
#m: [[4.]]
18. Basic TF 2 Operations
Working with Constants
Working with Strings
Working with Tensors
Working Operators
Arrays in TF 2
Multiplying Two Arrays
Convert Python Arrays to TF Arrays
Conflicting Types
19. TensorFlow “primitive types”
tf.constant:
> initialized immediately and immutable
import tensorflow as tf
aconst = tf.constant(3.0)
print(aconst)
# output for TF 2:
#tf.Tensor(3.0, shape=(), dtype=float32)
# output for TF 1.x:
#Tensor("Const:0", shape=(), dtype=float32)
20. TensorFlow “primitive types”
tf.Variable (a class):
+ initial value is required
+ updated during training
+ in-memory buffer (saved/restored from disk)
+ can be shared in a distributed environment
+ they hold learned parameters of a model
21. TensorFlow Arithmetic
import tensorflow as tf # arith1.py
a = tf.add(4, 2)
b = tf.subtract(8, 6)
c = tf.multiply(a, 3)
d = tf.div(a, 6)
print(a) # 6
print(b) # 2
print(c) # 18
print(d) # 1
print("a:",a.numpy())
print("b:",b.numpy())
print("c:",c.numpy())
print("d:",d.numpy())
26. A “while” Loop Example
import tensorflow as tf
a = tf.constant(12)
while not tf.equal(a, 1):
if tf.equal(a % 2, 0):
a = a / 2
else:
a = 3 * a + 1
print(a)
29. Some TF 2 “lazy operators”
map()
filter()
flatmap()
batch()
take()
zip()
flatten()
Combined via “method chaining”
30. TF 2 “lazy operators”
filter():
uses Boolean logic to "filter" the elements in an array to
determine which elements satisfy the Boolean condition
map(): a projection
this operator "applies" a lambda expression to each input
element
flat_map():
maps a single element of the input dataset to a Dataset of
elements
31. TF 2 “lazy operators”
batch(n):
processes a "batch" of n elements during each
iteration
repeat(n):
repeats its input values n times
take(n):
operator "takes" n input values
32. TF 2 tf.data.Dataset
TF “wrapper” class around data sources
Located in the tf.data.Dataset namespace
Supports lambda expressions
Supports lazy operators
They use generators instead of iterators (TF 1.x)
33. tf.data.Dataset.from_tensors()
Import tensorflow as tf
#combine the input into one element
t1 = tf.constant([[1, 2], [3, 4]])
ds1 = tf.data.Dataset.from_tensors(t1)
# output: [[1, 2], [3, 4]]
35. TF 2 Datasets: code sample
import tensorflow as tf # tf2-dataset.py
import numpy as np
x = np.arange(0, 10)
# create a dataset from a Numpy array
ds = tf.data.Dataset.from_tensor_slices(x)
36. TF 1.x Datasets: iterator
import tensorflow as tf # tf1x-plusone.py
import numpy as np
x = np.arange(0, 10)
# create dataset object from Numpy array
ds = tf.data.Dataset.from_tensor_slices(x)
ds.map(lambda x: x + 1)
# create a one-shot iterator <= TF 1.x
iterator = ds.make_one_shot_iterator()
for i in range(10):
val = iterator.get_next()
print("val:",val)
37. TF 2 Datasets: generator
import tensorflow as tf # tf2-plusone.py
import numpy as np
x = np.arange(0, 5) # 0, 1, 2, 3, 4
def gener():
for i in x:
yield (3*i)
ds = tf.data.Dataset.from_generator(gener, (tf.int64))
for value in ds.take(len(x)):
print("1value:",value)
for value in ds.take(2*len(x)):
print("2value:",value)
42. TF 2 map() and take()
import tensorflow as tf
import numpy as np
x = np.array([[1],[2],[3],[4]])
# make a ds from a numpy array
ds = tf.data.Dataset.from_tensor_slices(x)
ds = ds.map(lambda x: x*2).map(lambda x:
x+1).map(lambda x: x**3)
for value in ds.take(4):
print("value:",value)
44. TF 2 batch(): EXTRA CREDIT
import tensorflow as tf
import numpy as np
x = np.arange(0, 12)
def gener():
i = 0
while(i < len(x/3)):
yield (i, i+1, i+2) # three integers at a time
i += 3
ds = tf.data.Dataset.from_generator(gener, (tf.int64,tf.int64,tf.int64))
third = int(len(x)/3)
for value in ds.take(third):
print("value:",value)
45. TF 2 batch() & zip(): EXTRA CREDIT
import tensorflow as tf
ds1 = tf.data.Dataset.range(100)
ds2 = tf.data.Dataset.range(0, -100, -1)
ds3 = tf.data.Dataset.zip((ds1, ds2))
ds4 = ds3.batch(4)
for value in ds.take(10):
print("value:",value)
46. TF 2: Working with tf.keras
tf.keras.layers
tf.keras.models
tf.keras.optimizers
tf.keras.utils
tf.keras.regularizers
47. TF 2: tf.keras.models
tf.keras.models.Sequential(): most common
clone_model(...): Clone any Model instance
load_model(...): Loads a model saved via save_model
model_from_config(...): Instantiates a Keras model from its
config
model_from_json(...):
Parses a JSON model config file and returns a model instance
model_from_yaml(...):
Parses a yaml model config file and returns a model instance
save_model(...): Saves a model to a HDF5 file
55. Other TF 2 Namespaces
tf.keras.regularizers (L1 and L2)
tf.keras.utils (to_categorical)
tf.keras.preprocessing.text.Tokenizer
56. TF 1.x Model APIs
tf.core (“reduced” in TF 2)
tf.layers (deprecated in TF 2)
tf.keras (the primary API in TF 2)
tf.estimator.Estimator (implementation in tf.keras)
tf.contrib.learn.Estimator (Deprecated in TF 2)
57. Other TF 2 Features
TF Probability (Linear Regression)
TF Agents (Reinforcement Learning)
TF Text (NLP)
TF Federated
TF Privacy
Tensor2Tensor
TF Ranking
TFX
58. TF 2 (Keras), DL, and RL
The remaining slides briefly discuss TF 2 and:
CNNs (Convolutional Neural Networks)
RNNs (Recurrent Neural Networks)
LSTMs (Long Short Term Memory)
Autoencoders
Variational Autoencoders
Reinforcement Learning
Some Keras-based code blocks
Some useful links
62. Transition Between Layers
W = weight matrix between adjacent layers
x = an input vector of numbers
b = a bias vector of numbers
F = activation function (sigmoid, tanh, etc)
Y = output vector of numbers
Y = F(x*W + b)
67. What are RNNs?
RNNs = Recurrent Neural Networks
RNNs capture/retain events in time
FF networks do not learn from the past
=> RNNs DO learn from the past
RNNs contain layers of recurrent neurons
68. Common Types of RNNs
1) Simple RNNs (minimal structure)
2) LSTMs (input, forget, output gates)
3) Gated Recurrent Unit (GRU)
NB: RNNs can be used with MNIST data
69. Some Use Cases for RNNs
1) Time Series Data
2) NLP (Natural Language Processing):
Processing documents
“analyzing” Trump’s tweets
3) RNNs and MNIST dataset
74. RNN and MNIST Dataset
Sample initialization values for RNN:
n_steps = 28 # # of time steps
n_inputs = 28 # number of rows
n_neurons = 150 # 150 neurons in one layer
n_outputs = 10 # 10 digit classes (0->9)
Remember that:
one layer = one memory cell
MNIST class count = size of output layer
75. TF 2/Keras and RNNs
import tensorflow as tf
...
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.SimpleRNN(20,
input_shape=(input_length, input_dim),
return_sequences=False))
...
76. Problems with RNNs
lengthy training time
Unrolled RNN will be very deep
Propagating gradients through many layers
Prone to vanishing gradient
Prone to exploding gradient
77. What are LSTMs?
Long short-term memory (LSTM):
a model for the short-term memory
Contains more state than a “regular” RNN
which can last for a long period of time
can forget and remember things selectively
Hochreiter/Schmidhuber proposed in 1997
improved in 2000 by Felix Gers' team
set accuracy records in multiple apps
78. Features of LSTMs
Used in Google speech recognition + Alpha Go
they avoid the vanishing gradient problem
Can track 1000s of discrete time steps
Used by international competition winners
79. Use Cases for LSTMs
Connected handwriting recognition
Speech recognition
Forecasting
Anomaly detection
Pattern recognition
80. Advantages of LSTMs
well-suited to classify/process/predict time series
More powerful: increased memory (=state)
Can explicitly add long-term or short-term state
even with time lags of unknown size/duration between
events
81. The Primary LSTM Gates
Input, Forget, and Output gates (FIO)
The main operational block of LSTMs
an "information filter"
a multivariate input gate
some inputs are blocked
some inputs "go through"
"remember" necessary information
FIO gates use the sigmoid activation function
The cell state uses the tanh activation function
82. LSTM Gates and Cell State
1) the input gate controls:
the extent to which a new value flows into the cell state
2) the forget gate controls:
the extent to which a value remains in the cell state
3) the output gate controls:
The portion of the cell state that’s part of the output
4) the cell state maintains long-term memory
=> LSTMs store more memory than basic RNN cells
83. Common LSTM Variables
# LSTM unrolled through 28 time steps (with MNIST):
time_steps = 28
# number of hidden LSTM units:
num_units = 128
# number of rows of 28 pixels:
n_inputs = 28
# number of pixels per row:
input_size = 28
# mnist has 10 classes (0-9):
n_classes = 10
# size of input batch:
batch_size = 128
86. TF 2/Keras and LSTMs
import tensorflow as tf
. . .
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.LSTMCell(6,batch_input_shape=(1,1,1),kernel_i
nitializer='ones',stateful=True))
model.add(tf.keras.layers.Dense(1))
. . .
=> LSTM versus LSTMCell:
https://stackoverflow.com/questions/48187283/whats-the-difference-
between-lstm-and-lstmcell
=> How to define a custom LSTM cell:
https://stackoverflow.com/questions/54231440/define-custom-lstm-cell-in-
keras
87. TF 2/Keras and LSTM/RNN/GRU
import tensorflow as tf
...
model = Sequential()
model.add(tf.keras.layers.LSTM(64,
return_sequences=True, input_shape=(10, 64)))
model.add(tf.keras.layers.SimpleRNN(32,
return_sequences=True))
model.add(tf.keras.layers.GRU(10, ...)
...
96. Reinforcement Learning
Dopamine on TensorFlow (Research Framework):
https://github.com/google/dopamine
TF-Agents library for RL in TensorFlow:
https://github.com/tensorflow/agents
Keras and Reinforcement Learning:
https://github.com/keras-rl/keras-rl
OpenAI universe, DeepMind Lab, TensorForce
97. About Me: Recent Books
1) Python3 and Machine Learning (2020)
2) Angular 9 and Deep Learning (2020)
3) Angular 8 & Machine Learning (2020)
4) AI/ML/DL: Concepts and Code (2020)
5) Bash Programming on Mac (2020)
6) TensorFlow 2 Pocket Primer (2019)
7) TensorFlow 1.x Pocket Primer (2019)
8) Python for TensorFlow (2019)
9) C Programming Pocket Primer (2019)
98. About Me: Less Recent Books
10) RegEx Pocket Primer (2018)
11) Data Cleaning Pocket Primer (2018)
12) Angular Pocket Primer (2017)
13) Android Pocket Primer (2017)
14) CSS3 Pocket Primer (2016)
15) SVG Pocket Primer (2016)
16) Python Pocket Primer (2015)
17) D3 Pocket Primer (2015)
18) HTML5 Mobile Pocket Primer (2014)
99. About Me: Older Books
19) jQuery, CSS3, and HTML5 (2013)
20) HTML5 Pocket Primer (2013)
21) jQuery Pocket Primer (2013)
22) HTML5 Canvas (2012)
23) Flash on Android (2011)
24) Web 2.0 Fundamentals (2010)
25) MS Silverlight Graphics (2008)
26) Fundamentals of SVG (2003)
27) Java Graphics Library (2002)
100. What I do (Training)
ML/DL/RL Instructor at UCSC (Santa Clara):
Machine Learning (Python) (09/09/2019) 10 weeks
Deep Learning (TF2/Keras) (10/04/2019) 10 weeks
Deep Learning (Keras/TF 2) (01/30/2020) 10 weeks
DL, NLP, RL (Adv Topics) (TBD/2020) 10 weeks
NLP (Transformers, BERT, etc) (TBD/2020) 10 weeks
RL (PPO, A3C, SAC, etc) (TBD/2020) 10 weeks
UCSC link:
https://www.ucsc-extension.edu/certificate-program/offering/deep-learning-
and-artificial-intelligence-tensorflow