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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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Beginning Deep Learning with TensorFlow

TensorFlow 2 and Keras.;

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The Deep Learning Workshop

Features Understand how to implement deep learning with TensorFlow and Keras Learn the fundamentals of computer vision and image recognition;

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Step by Step Tutorials on Deep Learning Using Scikit-Learn, Keras, and Tensorflow with Python GUI

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Deep Learning mit TensorFlow, Keras und TensorFlow.js

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Beginning Deep Learning with TensorFlow

TensorFlow 2 and Keras. The materials used in this book are based on years of successful online education experience and feedback from thousands;

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Publisher's Note: This edition from 2018 is outdated and is not compatible with TensorFlow 2 or any of the most recent updates to Python;

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Artificial Intelligence, Machine Learning, and Deep Learning

This book begins with an introduction to AI, followed by machine learning, deep learning, NLP, and reinforcement learning. Readers will;

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Hands-On Deep Learning for Images with TensorFlow

all the concepts of deep learning and their implementation with TensorFlow and Keras. What you will learn Build machine learning models;

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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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Advanced Applied Deep Learning

-advanced Python and machine learning know-how. Additionally, intermediate knowledge of Keras and TensorFlow is expected.;

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Python 3 for Machine Learning

related to machine learning Includes separate appendices for regular expressions, Keras, and TensorFlow 2;

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

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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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Deep Learning with Applications Using Python

learning frameworks such as TensorFlow, Keras, and scikit-learn. Use face recognition and face detection capabilities Create speech-to-text;

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Advanced Deep Learning for Engineers and Scientists

effective implementation for scientists. They then present technical exposure towards deep learning using Keras, Tensorflow, Pytorch and 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 Python

Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. You'll learn directly;

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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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R Deep Learning Essentials

Implement neural network models in R 3.5 using TensorFlow, Keras, and MXNet Key Features Use R 3.5 for building deep learning models for;

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Deep Learning Projects Using TensorFlow 2

Work through engaging and practical deep learning projects using TensorFlow 2.0.;

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TensorFlow 2.0 Quick Start Guide

book is forData scientists, machine learning developers, and deep learning enthusiasts looking to quickly get started with TensorFlow 2 will;

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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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What's New in TensorFlow 2.0

Get to grips with key structural changes in TensorFlow 2.0 Key Features Explore TF Keras APIs and strategies to run GPUs, TPUs, and;

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Hands-On Deep Learning with Apache Spark

learning with Spark Use popular deep learning frameworks, such as Deeplearning4j, TensorFlow, and Keras Explore popular deep learning algorithms;

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Practical Deep Learning for Cloud and Mobile RealWorld AI Computer Vision Projects Using Python, Keras TensorFlow

something that people in the real world can use. Train, tune, and deploy computer vision models with Keras, TensorFlow, CoreML, and TensorFlow Lite;

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Principles and Labs for Deep Learning

, and TensorFlow Hub. Each chapter has one corresponding Lab with step-by-step instruction to help the reader practice and accomplish a specific;

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