Book Image

TensorFlow Deep Learning Projects

By : Alexey Grigorev, Rajalingappaa Shanmugamani
Book Image

TensorFlow Deep Learning Projects

By: Alexey Grigorev, Rajalingappaa Shanmugamani

Overview of this book

TensorFlow is one of the most popular frameworks used for machine learning and, more recently, deep learning. It provides a fast and efficient framework for training different kinds of deep learning models, with very high accuracy. This book is your guide to master deep learning with TensorFlow with the help of 10 real-world projects. TensorFlow Deep Learning Projects starts with setting up the right TensorFlow environment for deep learning. You'll learn how to train different types of deep learning models using TensorFlow, including Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Generative Adversarial Networks. While doing this, you will build end-to-end deep learning solutions to tackle different real-world problems in image processing, recommendation systems, stock prediction, and building chatbots, to name a few. You will also develop systems that perform machine translation and use reinforcement learning techniques to play games. By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow, and will be able to build and train your own deep learning models with TensorFlow confidently.
Table of Contents (12 chapters)

Summary

In this chapter, we built a very deep neural network with the help of TensorFlow in order to detect duplicated questions from the Quora dataset. The project allowed us to discuss, revise, and practice plenty of different topics previously seen in other chapters: TF-IDF, SVD, classic machine learning algorithms, Word2vec and GloVe embeddings, and LSTM models.

In the end, we obtained a model whose achieved accuracy is about 82.5%, a figure that is higher than traditional machine learning approaches and is also near other state-of-the-art deep learning solutions, as reported by the Quora blog.

It should also be noted that the models and approaches discussed in this chapter can easily be applied to any semantic matching problem.