The following topics will be covered as part of this series. Each topic is described in detail with hands-on exercises done on Python to help students learn with ease. We will cover all the nitty-gritty that you need to know to get started with Deep Learning along with the correction and handling of errors as and when they pop-up. The program builds a solid foundation by covering the most popular and widely used deep Learning technologies and its applications.
The topics that are covered in this tutorial are as follows:Introduction to Deep Learning
Origin of Deep Learning and Introduction to Artificial Neural Networks. (ANN)
Training Deep Neural Nets
Distributing TensorFlow Across Devices and Servers
Convolutional Neural Networks
PreliminariesAttention Mechanisms
Linear Neural Network (Linear Regression)
Softmax Regression
Multilayer Perceptrons
Dropout
Deep Learning Computation
Recurrent Neural Networks
Implementation of Recurrent Neural Networks
Attention Mechanism
Optimization Algorithm
From this course students will have a clear understanding about the Deep Learning algorithms with real world examples
For better understanding knowledge on Machine Learning is recommended.