Book Image

Deep Learning Essentials

By : Wei Di, Jianing Wei, Anurag Bhardwaj
3 (1)
Book Image

Deep Learning Essentials

3 (1)
By: Wei Di, Jianing Wei, Anurag Bhardwaj

Overview of this book

Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning. This book will help you take your first steps in training efficient deep learning models and applying them in various practical scenarios. You will model, train, and deploy different kinds of neural networks such as CNN, RNN, and will see some of their applications in real-world domains including computer vision, natural language processing, speech recognition, and so on. You will build practical projects such as chatbots, implement reinforcement learning to build smart games, and develop expert systems for image captioning and processing using Python library such as TensorFlow. This book also covers solutions for different problems you might come across while training models, such as noisy datasets, and small datasets. By the end of this book, you will have a firm understanding of the basics of deep learning and neural network modeling, along with their practical applications.
Table of Contents (12 chapters)

Practical examples

In this section, we provide a practical problem that can be solved using a neural network. We will introduce the problems and build our neural network model using TensorFlow to solve the problems.

TensorFlow setup and key concepts

We recommend readers follow the instructions at https://www.tensorflow.org/install/ to install TensorFlow. We will use Python as our programming language. There are mainly three key concepts that are used in the code sample:

  • Tensor: Tensor is the central data unit in TensorFlow. We may think of it as a matrix of any number of dimensions. The entries within the tensor are primitive values. For example, see the following:
5 is a scalar and a rank 0 tensor
[[0, 1, 2], [3, 4, 5]]...