**Autor: Paolo Galeone**

Broj strana: 358

ISBN broj: 9781789615555

Izdavač:
PACKT PUBLISHING

Godina izdanja: 2019.

Pregleda (30 dana / ukupno): **34 / 34**

Predlog za prevod

- Grasp machine learning and neural network techniques to solve challenging tasks
- Apply the new features of TF 2.0 to speed up development
- Use TensorFlow Datasets (tfds) and the tf.data API to build high-efficiency data input pipelines
- Perform transfer learning and fine-tuning with TensorFlow Hub
- Define and train networks to solve object detection and semantic segmentation problems
- Train Generative Adversarial Networks (GANs) to generate images and data distributions
- Use the SavedModel file format to put a model, or a generic computational graph, into production

TensorFlow, the most popular and widely used machine learning framework, has made it possible for almost anyone to develop machine learning solutions with ease. With TensorFlow (TF) 2.0, you'll explore a revamped framework structure, offering a wide variety of new features aimed at improving productivity and ease of use for developers. This book covers machine learning with a focus on developing neural network-based solutions. You'll start by getting familiar with the concepts and techniques required to build solutions to deep learning problems. As you advance, you’ll learn how to create classifiers, build object detection and semantic segmentation networks, train generative models, and speed up the development process using TF 2.0 tools such as TensorFlow Datasets and TensorFlow Hub. By the end of this TensorFlow book, you'll be ready to solve any machine learning problem by developing solutions using TF 2.0 and putting them into production.

- Understand the basics of machine learning and discover the power of neural networks and deep learning
- Explore the structure of the TensorFlow framework and understand how to transition to TF 2.0
- Solve any deep learning problem by developing neural network-based solutions using TF 2.0

**Table of contents**

**1 What is Machine Learning? **

The importance of the dataset

Supervised learning

Unsupervised learning

Semi-supervised learning

Summary

Exercises **2 Neural Networks and Deep Learning **

Neural networks

Optimization

Convolutional neural networks

Regularization

Summary

Exercises **3 TensorFlow Graph Architecture **

Environment setup

Dataflow graphs

Model definition and training

Interacting with the graph using Python

Summary

Exercises **4 TensorFlow 2.0 Architecture **

Relearning the framework

The Keras framework and its models

Eager execution and new features

Codebase migration

Summary

Exercises **5 Efficient Data Input Pipelines and Estimator API **

Efficient data input pipelines

Estimator API

Summary

Exercises **6 Image Classification Using TensorFlow Hub **

Getting the data

Transfer learning

Fine-tuning

Summary

Exercises **7 Introduction to Object Detection **

Getting the data

Object localization

Classification and localization

Summary

Exercises **8 Semantic Segmentation and Custom Dataset Builder **

Semantic segmentation

Create a TensorFlow DatasetBuilder

Model training and evaluation

Summary

Exercises **9 Generative Adversarial Networks **

Understanding GANs and their applications

Unconditional GANs

Conditional GANs

Summary

Exercises **10 Bringing a Model to Production **

The SavedModel serialization format

Python deployment

Supported deployment platforms

Summary

Exercises

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