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)

How a network learns

Suppose we have a two-layer network. Let’s represent inputs/outputs with , and the two layers by states, that is, the connection weights with bias value: and . We will also use σ as the activation function.

Weight initialization

After the configuration of the network, training starts with initializing the weights' values. A proper weight initialization is important in the sense that all the training does is to adjust the coefficients to best capture the patterns from data in order to successfully output the approximation of the target value. In most cases, weights are initialized randomly. In some finely-tuned settings, weights are initialized using a pre-trained model.

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