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)

Long short-term memory network

So far, we have seen that RNNs perform poorly due to the vanishing and exploding gradient problem. LSTMs are designed to help us overcome this limitation. The core idea behind LSTM is a gating logic, which provides a memory-based architecture that leads to an additive gradient effect instead of a multiplicative gradient effect as shown in the following figure. To illustrate this concept in more detail, let us look into LSTM's memory architecture. Like any other memory-based system, a typical LSTM cell consists of three major functionalities:

  • Write to memory
  • Read from memory
  • Reset memory
LSTM: Core idea (Source: https://ayearofai.com/rohan-lenny-3-recurrent-neural-networks-10300100899b)

Figure LSTM: Core idea illustrates this core idea. As shown in the figure LSTM: Core idea, first the value of a previous LSTM cell is passed through a reset...