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)

Applications

In this section, we talk about some example use cases and fine-tuning of NLP models.

Example use cases

With the pre-trained Word2Vec embedding, the downstream applications can be many for NLP, for example, document classification or sentiment classification. One example is called Doc2Vec, which, in the simplest form, takes the Word2Vec vectors of every word in the document and aggregates them by either taking a normalized sum or arithmetic mean of the terms. The resulting vector for each document is used for text classification. This type of application can be thought of as the direct application of the learned word embeddings.

On the other hand, we can apply the idea of Word2Vec modeling to other applications...