Beginners Guide To Implementing Neural Networks With Keras

Learn Keras in an hour

What you'll learn
How to Implement Deep Neural Network
How to Implement Convolutional Neural Network
How to Implement Recurrent Neural Network
How to Implement Complex Neural Network which has both CNN and RNN layers
Requirements
Student how know theoretical concepts of Deep Learning
Description
In this course, you will learn how to implement all major kinds of neural networks with hands-on projects in Keras. You will not need to set up anything on your system, Everything will be done online. You will be provided with Example code and practice problems. You are going to do the following projects- Implement and train a fully connected neural network for MNIST dataset, character classification. - Implement and train a Convolutional neural network for the MNISt dataset, character classification. -  Implement and train a Multi-Layer LSTM neural network for the WISDM dataset for Human Activity Recognition. -  Implement and train a Multi-Layer CNN-RNN kind of neural network for the WISDM dataset for Human Activity Recognition. For each of the projects, code is provided and Colab notebooks are shared which you can experiment with. This course is designed in a way to get started from the very basics and then reach a stage where you will be able to implement very recent and complex models. It is expected that you already have a theoretical background in deep learning a very basic knowledge would be enough to get started with this course. Hope you will like the course and will enjoy following it.

Overview

Section 1: Introduction

Lecture 1 Introduction to Colab

Section 2: Deep Neural Network

Lecture 2 Beginners guide to making Deep Neural Network with Keras in Colab

Section 3: Convolutional Neural Network

Lecture 3 Deep Neural Network with Keras

Section 4: Recurrent Neural network

Lecture 4 RNN with Keras

Section 5: Complex models Implementation

Lecture 5 Complex models with Keras

Beginners course for people interested in learning the implementation of Neural Networks and doing real world projects