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)

Challenges of multimodality learning

To use the multimodality information, we will face a few core challenges, such as representation, translation, alignment, fusion, and co-learning (non-exclusive). In this section, we will briefly talk about each of them.

Representation

Representation refers to the computer-interpretable description of the multimodal data (for example, vector and tensor). It covers the following, but is not limited to:

  • How to handle different symbols and signals—for example, in machine translation, Chinese characters and English characters are two distinct linguistic systems; in a self-driving system, point clouds from LIDAR sensors and image pixels from the RGB camera are two distinct sources with...