**Autor: Mirza Rahim Baig, Thomas V. Joseph, Et al**

Broj strana: 474

ISBN broj: 9781839219856

Izdavač:
PACKT PUBLISHING

Godina izdanja: 2020.

Predlog za prevod

- Understand how deep learning, machine learning, and artificial intelligence are different
- Develop multilayer deep neural networks with TensorFlow
- Implement deep neural networks for multiclass classification using Keras
- Train CNN models for image recognition
- Handle sequence data and use it in conjunction with RNNs
- Build a GAN to generate high-quality synthesized images

Are you fascinated by how deep learning powers intelligent applications such as self-driving cars, virtual assistants, facial recognition devices, and chatbots to process data and solve complex problems? Whether you are familiar with machine learning or are new to this domain, The Deep Learning Workshop will make it easy for you to understand deep learning with the help of interesting examples and exercises throughout. The book starts by highlighting the relationship between deep learning, machine learning, and artificial intelligence and helps you get comfortable with the TensorFlow 2.0 programming structure using hands-on exercises. You’ll understand neural networks, the structure of a perceptron, and how to use TensorFlow to create and train models. The book will then let you explore the fundamentals of computer vision by performing image recognition exercises with convolutional neural networks (CNNs) using Keras. As you advance, you’ll be able to make your model more powerful by implementing text embedding and sequencing the data using popular deep learning solutions. Finally, you’ll get to grips with bidirectional recurrent neural networks (RNNs) and build generative adversarial networks (GANs) for image synthesis. By the end of this deep learning book, you’ll have learned the skills essential for building deep learning models with TensorFlow and Keras.

- Understand how to implement deep learning with TensorFlow and Keras
- Learn the fundamentals of computer vision and image recognition
- Study the architecture of different neural networks

**Table of contents**

1 1. Building Blocks of Deep Learning

Introduction

Introduction to TensorFlow

Summary

2 2. Neural Networks

Introduction

Neural Networks and the Structure of Perceptrons

Training a Perceptron

Keras as a High-Level API

Exploring the Optimizers and Hyperparameters of Neural Networks

Activity 2.01: Build a Multilayer Neural Network to Classify Sonar Signals

Summary

3 3. Image Classification with Convolutional Neural Networks (CNNs)

Introduction

Digital Images

Image Processing

Convolutional Neural Networks

Pooling Layers

Data Augmentation

Saving and Restoring Models

Transfer Learning

Fine-Tuning

Summary

4 4. Deep Learning for Text – Embeddings

Introduction

Deep Learning for Natural Language Processing

Classical Approaches to Text Representation

Distributed Representation for Text

Summary

5 5. Deep Learning for Sequences

Introduction

Working with Sequences

Recurrent Neural Networks

Summary

6 6. LSTMs, GRUs, and Advanced RNNs

Introduction

Long-Range Dependence/Influence

The Vanishing Gradient Problem

Sequence Models for Text Classification

The Embedding Layer

Building the Plain RNN Model

Making Predictions on Unseen Data

LSTMs, GRUs, and Other Variants

Parameters in an LSTM

LSTM versus Plain RNNs

Gated Recurrence Units

Bidirectional RNNs

Stacked RNNs

Summarizing All the Models

Attention Models

More Variants of RNNs

Summary

7 7. Generative Adversarial Networks

Introduction

Deep Convolutional GANs

Summary

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