Hands-On Neural Networks with Keras

Key Features

• Design and create neural network architectures on different domains using Keras
• Integrate neural network models in your applications using this highly practical guide
• Get ready for the future of neural networks through transfer learning and predicting multi network models

Book Description

Neural networks are used to solve a wide range of problems in different areas of AI and deep learning.

Hands-On Neural Networks with Keras will start with teaching you about the core concepts of neural networks. You will delve into combining different neural network models and work with real-world use cases, including computer vision, natural language understanding, synthetic data generation, and many more. Moving on, you will become well versed with convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, autoencoders, and generative adversarial networks (GANs) using real-world training datasets. We will examine how to use CNNs for image recognition, how to use reinforcement learning agents, and many more. We will dive into the specific architectures of various networks and then implement each of them in a hands-on manner using industry-grade frameworks.

By the end of this book, you will be highly familiar with all prominent deep learning models and frameworks, and the options you have when applying deep learning to real-world scenarios and embedding artificial intelligence as the core fabric of your organization.

What you will learn

• Understand the fundamental nature and workflow of predictive data modeling
• Explore how different types of visual and linguistic signals are processed by neural networks
• Dive into the mathematical and statistical ideas behind how networks learn from data
• Design and implement various neural networks such as CNNs, LSTMs, and GANs
• Use different architectures to tackle cognitive tasks and embed intelligence in systems
• Learn how to generate synthetic data and use augmentation strategies to improve your models
• Stay on top of the latest academic and commercial developments in the field of AI

Who this book is for

This book is for machine learning practitioners, deep learning researchers and AI enthusiasts who are looking to get well versed with different neural network architecture using Keras. Working knowledge of Python programming language is mandatory.

主要特點

• 使用Keras在不同領域設計和創建神經網絡架構


• 通過這本高度實用的指南，在應用程序中集成神經網絡模型


• 通過轉移學習和預測多網絡模型，為神經網絡的未來做好準備

書籍描述

神經網絡被用於解決人工智能和深度學習不同領域的各種問題。

《使用Keras進行實踐神經網絡》將從教授您神經網絡的核心概念開始。您將深入研究結合不同神經網絡模型並處理真實世界的應用案例，包括計算機視覺、自然語言理解、合成數據生成等等。接下來，您將熟悉卷積神經網絡（CNN）、循環神經網絡（RNN）、長短期記憶（LSTM）網絡、自編碼器和生成對抗網絡（GAN）等，並使用真實的訓練數據集實現它們。我們將探討如何使用CNN進行圖像識別，如何使用強化學習代理等等。我們將深入研究各種網絡的具體架構，然後使用業界標準框架進行實踐。

通過閱讀本書，您將對所有重要的深度學習模型和框架非常熟悉，並了解在實際情境中應用深度學習和將人工智能作為組織核心的選項。

你將學到什麼

• 了解預測數據建模的基本性質和工作流程


• 探索神經網絡如何處理不同類型的視覺和語言信號


• 深入研究網絡如何從數據中學習的數學和統計思想


• 設計和實現各種神經網絡，如CNN、LSTM和GAN


• 使用不同的架構來應對認知任務並將智能嵌入系統中


• 學習如何生成合成數據並使用增強策略改進模型


• 緊跟人工智能領域的最新學術和商業發展

適合閱讀對象

本書適合機器學習從業人員、深度學習研究人員和人工智能愛好者，他們希望通過使用Keras熟悉不同神經網絡架構。需要具備Python編程語言的工作知識。

1. Overview of Neural Networks
2. A Deeper Dive into Neural Networks
3. Signal Processing - Data Analysis with Neural Networks
4. Convolutional Neural Networks
5. Recurrent Neural Networks
6. Long Short-Term Memory Networks
7. Reinforcement Learning with Deep Q-Networks
8. Autoencoders
9. Generative Networks
10. Contemplating Present and Future Developments

- 神經網絡概述
- 深入探討神經網絡
- 信號處理 - 使用神經網絡進行數據分析
- 卷積神經網絡
- 遞歸神經網絡
- 長短期記憶網絡
- 使用深度 Q 網絡進行強化學習
- 自編碼器
- 生成網絡
- 現在和未來發展的思考