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Mastering TensorFlow 1.x: Advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras
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About Mastering TensorFlow 1.x: Advanced Machine Learning
Build, scale, and deploy deep neural network models using the star libraries in PythonKey FeaturesDelve into advanced machine learning and deep learning use cases using Tensorflow and KerasBuild, deploy, and scale end-to-end deep neural network models in a production environmentLearn to deploy TensorFlow on mobile, and distributed TensorFlow on GPU, Clusters, and KubernetesBook DescriptionTensorFlow is the most popular numerical computation library built from the ground up for distributed, cloud, and mobile environments. TensorFlow represents the data as tensors and the computation as graphs.This book is a comprehensive guide that lets you explore the advanced features of TensorFlow 1.x. Gain insight into TensorFlow Core, Keras, TF Estimators, TFLearn, TF Slim, Pretty Tensor, and Sonnet. Leverage the power of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Throughout the book, you will obtain hands-on experience with varied datasets, such as MNIST, CIFAR-10, PTB, text8, and COCO-Images.You will learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF Clusters, deploy production models with TensorFlow Serving, and build and deploy TensorFlow models for mobile and embedded devices on Android and iOS platforms. You will see how to call TensorFlow and Keras API within the R statistical software and learn the required techniques for debugging when the TensorFlow API-based code does not work as expected.This book helps you obtain in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems. By the end of this guide, you will have mastered the offerings of TensorFlow and Keras, and gained the skills you need to build smarter, faster, and efficient machine learning and deep learning systems.What you will learnMaster advanced concepts of deep learning such as transfer learning, reinforcement learning, generative models and more, using TensorFlow and KerasPerform supervised (classification and regression) and unsupervised (clustering) learning to solve machine learning tasksBuild end-to-end deep learning (CNN, RNN, and Autoencoders) models with TensorFlowScale and deploy production models with distributed and high-performance computing on GPU and clustersBuild TensorFlow models to work with multilayer perceptrons using Keras, TFLearn, and RLearn the functionalities of smart apps by building and deploying TensorFlow models on iOS and Android devicesSupercharge TensorFlow with distributed training and deployment on Kubernetes and TensorFlow ClustersTable of ContentsTensorflow 101High Level Libraries for TensorFlowKeras 101Classical Machine Learning with TensorFlowNeural Networks and MLP with TensorFlow and KerasRNN with TensorFlow and KerasRNN for Time Series Data with TensorFlow and KerasNLP for Text Data with TensorFlow and KerasCNN with TensorFlow and KerasAutoencoder with TensorFlow and KerasTensorFlow Models in Production with TF ServingTransfer Learning and Pre-Trained ModelsDeep Reinforcement LearningGenerative Adversarial NetworksDistributed Models with TensorFlow ClustersTensorFlow on Mobile and Embedded PlatformsTensorFlow and Keras in RDebugging TensorFlow ModelsAppendix A: TPU
