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Machine Learning Concepts with Python and the Jupyter Notebook Environment

Ideal for Machine Learning and Deep Learning enthusiasts who are interested in programming with Python using Tensorflow 2.0 in the Jupyter;

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Tensorflow 2

(in Python) and figures. FEATURES: Provides an overview of the most important TensorFlow programming techniques used for machine learning, deep;

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Deep Learning with TensorFlow 2 and Keras

learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow;

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Machine Learning and Deep Learning Using Python and TensorFlow

Deep Learning Using Python and TensorFlow includes valuable case studies across professional domains, including banking, insurance, e-commerce;

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Machine Learning with TensorFlow 1.x

you how to easily get started with machine learning using the power of Python and TensorFlow 1.x. Firstly, you'll cover the basic;

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Python Machine Learning -

Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries;

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TensorFlow 2.0 Quick Start Guide

traditional supervised and unsupervised machine learning applications Understand image recognition techniques using TensorFlow Perform neural style;

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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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Hands-On Deep Learning for Images with TensorFlow

projects in TensorFlow. Knowledge of Python programming and basics of deep learning are required to get the best out of this book.;

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Python Deep Learning

Take your machine learning skills to the next level by mastering Deep Learning concepts and algorithms using Python. About This Book;

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Machine Learning Using TensorFlow Cookbook

. With the help of this book, you will be proficient in using TensorFlow, understand deep learning from the basics, and be able to implement;

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Learn TensorFlow 2.0

Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples. The book begins with;

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Learn TensorFlow 2.0

TensorFlow 2.0 framework and the major changes from its last release. Next, it focuses on building Supervised Machine Learning models using;

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The Deep Learning Workshop

learning, machine learning, and artificial intelligence and helps you get comfortable with the TensorFlow 2.0 programming structure using hands-on;

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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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Hands-On Convolutional Neural Networks with TensorFlow

of Convolutional Neural Networks Harness Python and Tensorflow to train CNNs Build scalable deep learning models that can process millions of items Book;

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Machine Learning Using R

industry focused real-world use cases Tackle time series modeling in R Apply deep learning using Keras and TensorFlow in R Who This Book is For;

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Python for Probability, Statistics, and Machine Learning

develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting;

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Python for Probability Statistics and Machine Learning

develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting;

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Python Reinforcement Learning Projects

using TensorFlow for RL Use RL algorithms in Python and TensorFlow to solve CartPole balancing Create deep reinforcement learning algorithms to;

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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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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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Practical Computer Vision Applications Using Deep Learning with CNNs

Will Learn Understand how ANNs and CNNs work Create computer vision applications and CNNs from scratch using Python Follow a deep learning;

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Hands-On Neural Networks with TensorFlow 2.0

learning and discover the power of neural networks and deep learning Explore the structure of the TensorFlow framework and understand how to;

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TensorFlow Deep Learning Projects

learning pipelines using the popular Tensorflow framework Train neural networks such as ConvNets, generative models, and LSTMs Includes projects;

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Machine Learning with TensorFlow

This fully revised second edition of Machine Learning with TensorFlow teaches you the foundational concepts of machine learning and how to;

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Hands-On Machine Learning with Scikit-Learn and TensorFlow

shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks-scikit-learn and TensorFlow-author;

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