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Generative AI with Python and TensorFlow 2

Fun and exciting projects to learn what artificial minds can create Key Features Code examples are in TensorFlow 2, which make it;

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Advanced Deep Learning with Keras

Publisher's Note: This edition from 2018 is outdated and is not compatible with TensorFlow 2 or any of the most recent updates to Python;

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Reinforcement Learning

learning and AI Work with Open AI Gym, Open AI, and Python Harness reinforcement learning with TensorFlow and Keras using Python Who This Book;

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Deep Learning with TensorFlow 2 and Keras

Build machine and deep learning systems with the newly released TensorFlow 2 and Keras for the lab, production, and mobile devices Key;

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Tensorflow 2

learning, linear and logical regression, etc. Covers TensorFlow 2 and previous versions that run with TF2. Includes companion files with source;

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Reinforcement Learning for Finance

This book introduces reinforcement learning with mathematical theory and practical examples from quantitative finance using the TensorFlow;

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Deep Learning Projects Using TensorFlow 2

develop a working knowledge of not only TensorFlow, but also related technologies such as Python and Keras. You'll also work with Neural Networks;

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Applied Reinforcement Learning with Python

Implement reinforcement learning with Python Work with AI frameworks such as OpenAI Gym, Tensorflow, and Keras Deploy and train reinforcement;

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Hands-On Convolutional Neural Networks with TensorFlow

Learn how to apply TensorFlow to a wide range of deep learning and Machine Learning problems with this practical guide on training CNNs for;

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Hands-On Image Generation with TensorFlow

Implement various state-of-the-art architectures, such as GANs and autoencoders, for image generation using TensorFlow 2.x from scratch;

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Python Advanced Guide to Artificial Intelligence: Advanced Guide to Artificial Intelligence: Expert machine learning systems and intelligent agents us

from your dataset and perform dimensionality reduction by making use of Python-based libraries. You'll bring the use of TensorFlow and Keras to;

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Generative Adversarial Networks Cookbook

Simplify next-generation deep learning by implementing powerful generative models using Python, TensorFlow and Keras Key Features;

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Deep Learning with JavaScript

, now JavaScript developers can build deep learning apps without relying on Python or R. Deep Learning with JavaScript shows;

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TensorFlow 2 x in the Colaboratory Cloud

-Python, TensorFlow 2.x, GPU support, and Jupyter Notebooks-is provided and ready to go from Colab. The book begins with an introduction to;

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Machine Learning mit Python und Keras, TensorFlow 2 und Scikit-learn

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Hands-On Artificial Intelligence for Beginners

TensorFlow in order to create powerful AI systems. You will begin with reviewing the recent changes in AI and learning how artificial neural networks;

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TensorFlow Deep Learning Projects

techniques to play games. By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow;

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Hands-On Computer Vision with TensorFlow 2

more Key Features Discover how to build, train, and serve your own deep neural networks with TensorFlow 2 and Keras Apply modern;

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State-of-the-Art Deep Learning Models in TensorFlow

connection. The Colab ecosystem provides everything else that you need, including Python, TensorFlow 2.x, GPU and TPU support, and Jupyter Notebooks;

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Practical Explainable AI Using Python

using frameworks such as Python XAI libraries, TensorFlow 2.0+, Keras, and custom frameworks using Python wrappers. You'll begin with an;

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Python Machine Learning

Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning;

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Machine Learning Concepts with Python and the Jupyter Notebook Environment

Create, execute, modify, and share machine learning applications with Python and TensorFlow 2.0 in the Jupyter Notebook environment. This;

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Applied Deep Learning with TensorFlow 2

Understand how neural networks work and learn how to implement them using TensorFlow 2.0 and Keras. This new edition focuses on the;

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Applied Deep Learning with TensorFlow 2

tutorials. This book is for: Readers with an intermediate understanding of machine learning, linear algebra, calculus, and basic Python programming.;

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Deep Learning with Python

TensorFlow machine-learning framework. He also does AI research, with a focus on abstraction and reasoning. His papers have been published at major;

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TensorFlow 2 Reinforcement Learning Cookbook

intelligence applications. Starting with an introduction to the fundamentals of deep reinforcement learning and TensorFlow 2.x, the book covers;

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Python 3 for Machine Learning

separate appendices for regular expressions, Keras, and TensorFlow 2. Features: Provides the reader withbasic Python3 programming concepts;

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