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Deep Learning With Tensorflow 2 And Keras Second Edition: Regression, Convnets, Gans, Rnns, Nlp, And More With Tensorflow 2 An
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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 Descriptiondeep 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 Forthis Book Is For Python Developers And Data Scientists Who Want To Build Machine Learning And Deep Learning Systems With Tensorflow. This Book Gives You The Theory And Practice Required To Use Keras, Tensorflow 2, And Automl To Build Machine Learning Systems. Some Knowledge Of Machine Learning Is Expected. 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
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- Q: How many pages does this book have? A: This book has six hundred forty-six pages. It provides extensive information on deep learning techniques and applications.
- Q: What is the binding type of this book? A: This book is published in paperback binding. This makes it lightweight and easy to handle.
- Q: What are the dimensions of this book? A: The book measures nine point twenty-five inches in length, seven point fifty-two inches in width, and nine point twenty-five inches in height. These dimensions make it a convenient size for reading.
- Q: Who is the author of this book? A: The author of this book is Antonio Gulli. He brings expertise in machine learning and deep learning.
- Q: What is the main subject of this book? A: The main subject of this book is deep learning with TensorFlow 2 and Keras. It covers various techniques and applications in this field.
- Q: How do I get started with TensorFlow using this book? A: You can get started by following the introductory chapters that explain TensorFlow basics. The book includes practical examples to help you apply what you learn.
- Q: Is this book suitable for beginners? A: Yes, this book is suitable for beginners. It introduces fundamental concepts in machine and deep learning clearly.
- Q: What programming language do I need to use this book? A: You need to know Python to effectively use this book. The examples and code are provided in Python.
- Q: Can I use this book for mobile application development? A: Yes, this book covers TensorFlow for mobile applications. It guides you on deploying models in mobile environments.
- Q: How should I store this book? A: You should store this book in a dry, cool place to prevent damage. Keeping it upright on a shelf is recommended.
- Q: Is there a warranty for this book? A: No, books typically do not come with a warranty. However, you can check for return policies with the retailer.
- Q: What if I receive a damaged book? A: If you receive a damaged book, you should contact the retailer for a replacement or return. Most retailers have policies in place for such issues.
- Q: Are there any exercises in this book? A: Yes, this book includes exercises and examples. These are designed to reinforce the concepts covered in each chapter.
- Q: Does this book cover advanced topics in deep learning? A: Yes, the book covers advanced topics such as Generative Adversarial Networks and Reinforcement Learning. It's comprehensive in its approach.
- Q: Is this book focused on natural language processing? A: Yes, this book includes sections on natural language processing. It teaches how to apply deep learning techniques to NLP tasks.
- Q: Can I use this book for academic purposes? A: Yes, this book is suitable for academic purposes. It provides theoretical knowledge along with practical applications.