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,"