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)

Traditional NLP

Extracting useful information for text-based information is no easy task. For a basic application, such as document classification, the common way of feature extraction is called bag of words (BoW), in which the frequency of the occurrence of each word is used as a feature for training the classifier. We will briefly talk about BoW in the following section, as well as the tf-idf approach, which is intended to reflect how important a word is to a document in a collection or corpus.

Bag of words

BoW is mainly for categorizing documents. It is also used in computer vision. The idea is to represent the document as a bag or a set of words, disregarding the grammar and the order of the word sequences.

After the preprocessing...