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)

Multilayer perceptrons

The multilayer perceptron is one of the simplest networks. Essentially, it is defined as having one input layer, one output layer, and a few hidden layers (more than one). Each layer has multiple neurons and the adjacent layers are fully connected. Each neuron can be thought of as a cell in these huge networks. It determines the flow and transformation of the incoming signals. Signals from the previous layers are pushed forward to the neuron of the next layer through the connected weights. For each artificial neuron, it calculates a weighted sum of all incoming inputs by multiplying the signal with the weights and adding a bias. The weighted sum will then go through a function called an activation function to decide whether it should be fired or not, which results in output signals for the next level.

For example, a fully-connected, feed-forward neural network...