Neural Network Programming with TensorFlow: Unleash the power of TensorFlow to train efficient neural networks

Neural Networks and their implementation decoded with TensorFlow

About This Book

• Develop a strong background in neural network programming from scratch, using the popular Tensorflow library.
• Use Tensorflow to implement different kinds of neural networks – from simple feedforward neural networks to multilayered perceptrons, CNNs, RNNs and more.
• A highly practical guide including real-world datasets and use-cases to simplify your understanding of neural networks and their implementation.

Who This Book Is For

This book is meant for developers with a statistical background who want to work with neural networks. Though we will be using TensorFlow as the underlying library for neural networks, book can be used as a generic resource to bridge the gap between the math and the implementation of deep learning. If you have some understanding of Tensorflow and Python and want to learn what happens at a level lower than the plain API syntax, this book is for you.

What You Will Learn

• Learn Linear Algebra and mathematics behind neural network.
• Dive deep into Neural networks from the basic to advanced concepts like CNN, RNN Deep Belief Networks, Deep Feedforward Networks.
• Explore Optimization techniques for solving problems like Local minima, Global minima, Saddle points
• Learn through real world examples like Sentiment Analysis.
• Train different types of generative models and explore autoencoders.
• Explore TensorFlow as an example of deep learning implementation.

In Detail

If you're aware of the buzz surrounding the terms such as "machine learning,"

「神經網絡及其在TensorFlow中的實現解析」

關於本書

- 從頭開始建立強大的神經網絡編程基礎，使用流行的TensorFlow庫。
- 使用TensorFlow實現不同類型的神經網絡，從簡單的前饋神經網絡到多層感知器、卷積神經網絡、循環神經網絡等等。
- 提供實用指南，包括真實世界的數據集和使用案例，以簡化對神經網絡及其實現的理解。

適合閱讀對象

本書適合具有統計背景且希望使用神經網絡的開發人員。儘管我們將使用TensorFlow作為神經網絡的底層庫，但本書也可以作為一個通用資源，用於填補數學和深度學習實現之間的差距。如果您對TensorFlow和Python有一定的了解，並且希望了解比純粹的API語法更低層次的內容，那麼本書適合您。

學習內容

- 學習線性代數和神經網絡背後的數學知識。
- 深入研究神經網絡，從基礎概念到高級概念，如卷積神經網絡、循環神經網絡、深度置信網絡和深度前饋網絡。
- 探索解決問題的優化技術，如局部極小值、全局極小值、鞍點。
- 通過情感分析等真實世界示例進行學習。
- 訓練不同類型的生成模型，並探索自編碼器。
- 探索TensorFlow作為深度學習實現的示例。

詳細內容

如果您對「機器學習」等詞語的熱潮有所了解，那麼您可能已經聽說過神經網絡。