Book Image

Neural Network Programming with Tensorflow

By : Manpreet Singh Ghotra, Rajdeep Dua
Book Image

Neural Network Programming with Tensorflow

By: Manpreet Singh Ghotra, Rajdeep Dua

Overview of this book

If you're aware of the buzz surrounding the terms such as "machine learning," "artificial intelligence," or "deep learning," you might know what neural networks are. Ever wondered how they help in solving complex computational problem efficiently, or how to train efficient neural networks? This book will teach you just that. You will start by getting a quick overview of the popular TensorFlow library and how it is used to train different neural networks. You will get a thorough understanding of the fundamentals and basic math for neural networks and why TensorFlow is a popular choice Then, you will proceed to implement a simple feed forward neural network. Next you will master optimization techniques and algorithms for neural networks using TensorFlow. Further, you will learn to implement some more complex types of neural networks such as convolutional neural networks, recurrent neural networks, and Deep Belief Networks. In the course of the book, you will be working on real-world datasets to get a hands-on understanding of neural network programming. You will also get to train generative models and will learn the applications of autoencoders. By the end of this book, you will have a fair understanding of how you can leverage the power of TensorFlow to train neural networks of varying complexities, without any hassle. While you are learning about various neural network implementations you will learn the underlying mathematics and linear algebra and how they map to the appropriate TensorFlow constructs.
Table of Contents (17 chapters)
Title Page
About the Authors
About the Reviewer
Customer Feedback

Large-scale video processing with neural networks

In this paper,, the authors explore how CNNs could be used for large-scale video classification. In this use case, the neural networks have access to not only the appearance information in single, static images, but also the complex temporal evolution of the image. There are several challenges in extending and applying CNNs in this setting.

There are very few (or none) video classification benchmarks that match the scale and variety of existing image datasets as videos are significantly more challenging to collect, annotate, and store. To obtain sufficient amount of data needed to train our CNN architectures, authors collected a new Sports-1M dataset. This dataset contains 1 million videos (from YouTube) belonging to a taxonomy of 487 classes of sports. Sports-1M is also available to the research community to support future work in this area.

In this work...