Book Image

Deep Learning By Example

Book Image

Deep Learning By Example

Overview of this book

Deep learning is a popular subset of machine learning, and it allows you to build complex models that are faster and give more accurate predictions. This book is your companion to take your first steps into the world of deep learning, with hands-on examples to boost your understanding of the topic. This book starts with a quick overview of the essential concepts of data science and machine learning which are required to get started with deep learning. It introduces you to Tensorflow, the most widely used machine learning library for training deep learning models. You will then work on your first deep learning problem by training a deep feed-forward neural network for digit classification, and move on to tackle other real-world problems in computer vision, language processing, sentiment analysis, and more. Advanced deep learning models such as generative adversarial networks and their applications are also covered in this book. By the end of this book, you will have a solid understanding of all the essential concepts in deep learning. With the help of the examples and code provided in this book, you will be equipped to train your own deep learning models with more confidence.
Table of Contents (18 chapters)
16
Implementing Fish Recognition

Introduction to representation learning

All the machine learning algorithms or architectures that we have used so far require the input to be real-valued or matrices of real-valued quantities, and that's a common theme in machine learning. For example, in the convolution neural network, we had to feed raw pixel values of images as model inputs. In this part, we are dealing with text, so we need to encode our text somehow and produce real-valued quantities that can be fed to a machine learning algorithm. In order to encode input text as real-valued quantities, we need to use an intermediate science called Natural Language Processing (NLP).

We mentioned that in this kind of pipeline, where we feed text to a machine learning model such as sentiment analysis, this will be problematic and won't work because we won't be able to apply backpropagation or any other operations...