[Télécharger] Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API, 2nd Edition de Antonio Gulli Livre eBook France
Télécharger Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API, 2nd Edition de Antonio Gulli Francais PDF
Télécharger "Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API, 2nd Edition" de Antonio Gulli En Ligne
Auteur : Antonio Gulli
Catégorie : Livres anglais et étrangers,Computers & Internet,Software
Broché : * pages
Éditeur : *
Langue : Français, Anglais
Build machine and deep learning systems with the newly released TensorFlow 2 and Keras for the lab, production, and mobile devices Key Features Introduces and then uses TensorFlow 2 and Keras right from the start Teaches key machine and deep learning techniques Understand the fundamentals of deep learning and machine learning through clear explanations and extensive code samples Book Description Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You'll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before. This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML. What you will learn Build machine learning and deep learning systems with TensorFlow 2 and the Keras API Use Regression analysis, the most popular approach to machine learning Understand ConvNets (convolutional neural networks) and how they are essential for deep learning systems such as image classifiers Use GANs (generative adversarial networks) to create new data that fits with existing patterns Discover RNNs (recurrent neural networks) that can process sequences of input intelligently, using one part of a sequence to correctly interpret another Apply deep learning to natural human language and interpret natural language texts to produce an appropriate response Train your models on the cloud and put TF to work in real environments Explore how Google tools can automate simple ML workflows without the need for complex modeling Who this book is for This book is for Python developers and data scientists who want to build machine learning and deep learning systems with TensorFlow. Whether or not you have done machine learning before, this book gives you the theory and practice required to use Keras, TensorFlow 2, and AutoML to build machine learning systems.Table of Contents Neural Network Foundations with TensorFlow 2.0 TensorFlow 1.x and 2.x Regression Convolutional Neural Networks Advanced Convolutional Neural Networks Generative Adversarial Networks Word Embeddings Recurrent Neural Networks Autoencoders Unsupervised Learning Reinforcement Learning TensorFlow and Cloud TensorFlow for Mobile and IoT and TensorFlow.js An introduction to AutoML The Math Behind Deep Learning Tensor Processing Unit
Télécharger Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API, 2nd Edition de Antonio Gulli Livre eBook France
Deep Learning with TensorFlow 2 and Keras - 2nd Edition ~ Deep Learning with TensorFlow 2 and Keras, 2nd edition teaches deep learning techniques alongside TensorFlow (TF) and Keras. The book introduces neural networks with TensorFlow, runs through the main applications, covers two working example apps, and then dives into TF and cloudin production, TF mobile, and using TensorFlow with AutoML.
Deep Learning with TensorFlow 2 and Keras - Second Edition ~ Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You’ll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and .
Advanced Deep Learning with TensorFlow 2 and Keras ~ Advanced Deep Learning with TensorFlow 2 and Keras (Updated for 2nd Edition) This is the code repository for Advanced Deep Learning with TensoFlow 2 and Keras, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish. Please note that the code examples have been updated to support TensorFlow 2.0 Keras API only. About the Book .
��' Gratis Deep Learning With TensorFlow 2 And Keras ~ ��' Gratis Deep Learning With TensorFlow 2 And Keras: Regression, ConvNets, GANs, RNNs, NLP, And More With TensorFlow 2 And The Keras API, 2nd Edition Author: ��www.liceolefilandiere Subject
Complete Tensorflow 2 and Keras Deep Learning Bootcamp / Udemy ~ Keras, a user-friendly API standard for machine learning, will be the central high-level API used to build and train models. The Keras API makes it easy to get started with TensorFlow 2. Importantly, Keras provides several model-building APIs (Sequential, Functional, and Subclassing), so you can choose the right level of abstraction for your project. TensorFlow’s implementation contains .
TensorFlow 2 Tutorial: Get Started in Deep Learning With ~ Predictive modeling with deep learning is a skill that modern developers need to know. TensorFlow is the premier open-source deep learning framework developed and maintained by Google. Although using TensorFlow directly can be challenging, the modern tf.keras API beings the simplicity and ease of use of Keras to the TensorFlow project.
TensorFlow and Keras Projects for Beginners / Coursera ~ TensorFlow is the one of most popular machine learning frameworks, and Keras is a high level API for deep learning which can be used with TensorFlow framework as its backend. In the first few Guided Projects of this collection, you can try out simple tasks like basic image classification and regression to help you build confidence with foundational topics and then move into more advanced tasks .
Recurrent Neural Networks (RNN) with Keras / TensorFlow Core ~ The Keras RNN API is designed with a focus on: Ease of use: . In TensorFlow 2.0, the built-in LSTM and GRU layers have been updated to leverage CuDNN kernels by default when a GPU is available. With this change, the prior keras.layers.CuDNNLSTM/CuDNNGRU layers have been deprecated, and you can build your model without worrying about the hardware it will run on. Since the CuDNN kernel is .
Keras: the Python deep learning API ~ Keras is the most used deep learning framework among top-5 winning teams on Kaggle. Because Keras makes it easier to run new experiments, it empowers you to try more ideas than your competition, faster. And this is how you win.
Introduction to Python Deep Learning with Keras ~ Two of the top numerical platforms in Python that provide the basis for Deep Learning research and development are Theano and TensorFlow. Both are very powerful libraries, but both can be difficult to use directly for creating deep learning models. In this post, you will discover the Keras Python library that provides a clean and convenient way to create a range of
Deep Learning with Keras: Implementing deep learning ~ A new second edition, updated for 2020 and featuring TensorFlow 2, the Keras API, CNNs, GANs, RNNs, NLP, and AutoML, has now been published. Key Features. Implement various deep learning algorithms in Keras and see how deep learning can be used in games; See how various deep learning models and practical use-cases can be implemented using Keras; A practical, hands-on guide with real-world .
The Functional API - Keras: the Python deep learning API ~ Introduction. The Keras functional API is a way to create models that are more flexible than the tf.keras.Sequential API. The functional API can handle models with non-linear topology, shared layers, and even multiple inputs or outputs. The main idea is that a deep learning model is usually a directed acyclic graph (DAG) of layers.
CoderProg - Ebooks & Elearning For Programming ~ Deep Learning with TensorFlow 2.0 [2020] December 23, 2020. Deep Learning with TensorFlow 2.0 [2020] English / MP4 / AVC 1280×720 / AAC 44KHz 2ch / 6 Hours / 1.87 GB. kubectl: Command-Line Kubernetes in a Nutshell: Deploy, manage, and debug container workloads using the Kubernetes CLI. December 23, 2020 . kubectl: Command-Line Kubernetes in a Nutshell: Deploy, manage, and debug container .
Hands-On Machine Learning with Scikit-Learn, Keras, and ~ Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition / Aurélien Géron / download / Z-Library. Download books for free. Find books
Electronic library. Download books free. Finding books ~ Electronic library. Download books free. Finding books / Z-Library. Download books for free. Find books
TensorFlow ~ TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.
Python Machine Learning - Third Edition / Packt ~ Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis .
TensorFlow 2.0 Practical Advanced / Udemy ~ This course will cover advanced, state-of-the–art AI models implementation in TensorFlow 2.0 such as DeepDream, AutoEncoders, Generative Adversarial Networks (GANs), Transfer Learning using TensorFlow Hub, Long Short Term Memory (LSTM) Recurrent Neural Networks and many more. The applications of these advanced AI models are endless including new realistic human photographs generation, text .
RStudio AI Blog ~ Many fields are benefiting from the use of deep learning, and with the R keras, tensorflow and related packages, you can now easily do state of the art deep learning in R. In this post, we want to give some orientation as to how to best get started. Aug. 26, 2018. Sigrid Keydana. Generating images with Keras and TensorFlow eager execution. TensorFlow/Keras. Unsupervised Learning. Image .
Hands-On Machine Learning with Scikit-Learn, Keras, and ~ Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems Aurélien Géron Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning.
Hands-On Machine Learning with Scikit-Learn, Keras, and ~ Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, … - Selection from Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition [Book]
CS224d: TensorFlow Tutorial - Deep Learning ~ PSet 2 Released tomorrow 4/20 (due 5/5) Help us help you! Fill out class survey to give us feedback. Qiaojing will host Tensorflow on AWS setup session in office hours, Sundar 4/24, 4-6 pm, Gates B24 Will host special TensorFlow help session in my office hours, Tuesday 4/26, 1-3 pm, Huang basement.
D2L ~ D2L
Manning / Deep Learning with Python ~ Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing .
Comments
Post a Comment