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.

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Table of Contents (12 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Why Deep Learning?
Why Deep Learning?
What is AI and deep learning?
The history and rise of deep learning
Why deep learning?
The motivation of deep architecture
Applications
Future potential and challenges
Summary
2
Getting Yourself Ready for Deep Learning
Getting Yourself Ready for Deep Learning
Basics of linear algebra
Deep learning with GPU
Deep learning software frameworks
Setting up deep learning on AWS
Summary
3
Getting Started with Neural Networks
Getting Started with Neural Networks
Multilayer perceptrons
How a network learns
Deep learning models
Practical examples
Summary
4
Deep Learning in Computer Vision
Deep Learning in Computer Vision
Origins of CNNs
Convolutional Neural Networks
Fine-tuning CNNs
Popular CNN architectures
Summary
5
NLP - Vector Representation
NLP - Vector Representation
Traditional NLP
Deep learning NLP
Applications
Summary
6
Advanced Natural Language Processing
Advanced Natural Language Processing
Deep learning for text
Recurrent neural networks
Long short-term memory network
Applications
Summary
7
Multimodality
Multimodality
What is multimodality learning?
Challenges of multimodality learning
Image captioning
Visual question answering
Multi-source based self-driving
Summary
8
Deep Reinforcement Learning
Deep Reinforcement Learning
What is reinforcement learning (RL)?
Deep reinforcement learning
Implementing reinforcement learning
Summary
9
Deep Learning Hacks
Deep Learning Hacks
Massaging your data
Tricks in training
Fine-tuning
Model compression
Summary
10
Deep Learning Trends
Deep Learning Trends
Recent models for deep learning
Novel applications
Summary
11
Other Books You May Enjoy
Other Books You May Enjoy
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Multi-source based self-driving

Is autonomous driving far from us? Perhaps not. If you look at the following road map published by Morgan Stanley Research, it is expected that in around 10-15 years we can safely let cars drive for us without much interference:

Great applications need powerful tools to realize them, and deep learning has been recognized as one such tool. Deep learning can potentially help in solving many problems that exist in the self-driving car system, for example:

  • Lane detection
  • Pedestrian detection
  • Road sign recognition
  • Traffic light detection
  • Face detection/recognition
  • Car detection
  • Obstacle detection
  • Environment recognition
  • Human action recognition
  • Blind-spot monitoring

And for all of these problems, it’s possible that we can leverage multiple data sources to gain better accuracy and speed. Basically, a self-driving car can be equipped with various...

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