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Deep Learning with Keras

Get to grips with the basics of Keras to implement fast and efficient deep-learning models About This Book * Implement various deep;

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The Deep Learning with Keras Workshop

learning from scratch with Keras, and be on your way to training your first ever neural networks. What sets Keras apart from other deep learning;

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Learn Keras for Deep Neural Networks

Learn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses;

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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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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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Neural Networks with Keras Cookbook

multiple neural network architectures such as CNN, RNN, LSTM in Keras Discover tips and tricks for designing a robust neural network to solve real;

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Machine Learning Using TensorFlow Cookbook

solutions from Kaggle Masters and Google Developer Experts Get to grips with the fundamentals including variables, matrices, and data sources;

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Advanced Deep Learning with R

speed with artificial neural networks, recurrent neural networks, convolutional neural networks, long short-term memory networks, and more using;

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Advanced Applied Deep Learning

, you will discover how to work efficiently with Keras with many tricks and tips, including how to customize logging in Keras with custom;

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R Deep Learning Projects

with packages such as deepnet, MXNetR, Tensorflow, H2O, Keras, and text2vec Apply neural networks to perform handwritten digit recognition;

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Hands-On Neural Networks

Design and create neural networks with deep learning and artificial intelligence principles using OpenAI Gym, TensorFlow, and Keras Key;

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Recurrent Neural Networks

scratch. It provides a treatment of the general recurrent neural networks with principled methods for training that render the (generalized;

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Java Deep Learning Cookbook

book is for you. Basic understanding of Java programming as well as some experience with machine learning and neural networks is required to;

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Deep Learning Quick Reference

with deep neural networks. What you will learn Solve regression and classification challenges with TensorFlow and Keras Learn to use Tensor;

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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

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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Deep Learning with Applications Using Python

Keras. This book helps you to ramp up your practical know-how in a short period of time and focuses you on the domain, models, and algorithms;

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Principles and Labs for Deep Learning

become proficient in TensorFlow to build deep Convolutional Neural Networks (CNNs) through custom APIs, high-level Keras APIs, Keras Applications;

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Advanced Deep Learning with Keras

models with Keras and TensorFlow 1.x, and move forwards to advanced techniques, as you explore deep neural network architectures, including;

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TensorFlow 1.x Deep Learning Cookbook

Take the next step in implementing various common and not-so-common neural networks with Tensorflow 1.x About This Book * Skill up;

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Deep Learning for Natural Language Processing

Gain the knowledge of various deep neural network architectures and their application areas to conquer your NLP issues. Key Features Gain;

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Hands-On Deep Learning with Apache Spark

learning with Apache Spark Train neural networks with deep learning libraries such as BigDL and TensorFlow Develop Spark deep learning applications;

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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 Python

learning in Python. He has been working with deep neural networks since 2012. Francois is currently doing deep learning research at Google. He;

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Angular and Deep Learning Pocket Primer

about MLPs (Multi Layer Perceptrons), CNNs (Convolutional Neural Networks), and a Keras-based code sample of a CNN with the MNIST dataset. The;

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TensorFlow 2.0 Quick Start Guide

techniques such as training neural networks Get acquainted with some new practices introduced in TensorFlow 2.0 Alpha Book DescriptionTensorFlow;

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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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