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)

Matrix factorization for recommender systems

In this section, we will go over traditional techniques for recommending systems. As we will see, these techniques are really easy to implement in TensorFlow, and the resulting code is very flexible and easily allows modifications and improvements.

For this section, we will use the Online Retail Dataset. We first define the problem we want to solve and establish a few baselines. Then we implement the classical Matrix factorization algorithm as well as its modification based on Bayesian Personalized Ranking.

Dataset preparation and baseline

Now we are ready to start building a recommender system.

First, declare the imports:

import tensorflow as tf
import pandas as pd
import numpy as...