In this book, you will learn how to efficiently use TensorFlow, Google's open source framework for deep learning. You will implement different deep learning networks such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learning Networks (DQNs), and Generative Advers
TensorFlow 1.x Deep Learning Cookbook
โ Scribed by Antonio Gulli
- Publisher
- Packt
- Year
- 2017
- Tongue
- English
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Synopsis
In this book, you will learn how to efficiently use TensorFlow, Google's open source framework for deep learning. You will implement different deep learning networks such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learning Networks (DQNs), and Generative Adversarial Networks (GANs) with easy to follow independent recipes.
๐ SIMILAR VOLUMES
Take the next step in implementing various common and not-so-common neural networks with Tensorflow 1.x About This Book Skill up and implement tricky neural networks using Google's TensorFlow 1.x An easy-to-follow guide that lets you explore reinforcement learning, GANs, autoencoders, multilayer p
Build, scale, and deploy deep neural network models using the star libraries in PythonKey Features Delve into advanced machine learning and deep learning use cases using Tensorflow and Keras Build, deploy, and scale end-to-end deep neural network models in a production environment Learn to deploy Te
<span><p><b>Build, scale, and deploy deep neural network models using the star libraries in Python</b></p><p><b>About This Book</b></p><ul><li>Delve into advanced machine learning and deep learning use cases using Tensorflow and Keras</li><li>Build, deploy, and scale end-to-end deep neural network m
Take the next step in implementing various common and not-so-common neural networks with Tensorflow 1.x Key Features Skill up and implement tricky neural networks using Google's TensorFlow 1.x An easy-to-follow guide that lets you explore reinforcement learning, GANs, autoencoders, multilayer p