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Introduction to Deep Learning

Step into the in-demand field of Deep Learning with the help of this Introduction to Deep Learning course for free that familiarizes you with its fundamentals and the significant concepts with relevant hands-on examples.

4.46
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Beginner

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2.25 Hrs

Learning hours

1.2L+
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About this Course

This online Deep Learning course aims to familiarize learners with all the crucial Deep Learning concepts currently being utilized to solve real-world problems. You will learn about the history and applications of Deep Learning and understand the role of the second wave in DL. Also, comprehend how ML differs from DL, go through the essential terms in Deep Learning called artificial neural networks, and comprehend the Deep Learning fundamentals.

 

You will also go through the demo on Tensorflow Playground, CNN, and neural networks. Learn about the involvement of a basic set of layers in DL and learn about activation function and CNN. Gain knowledge regarding RNN, LSTM, types of chatbots, and conventional interfaces. Dig deeper into the concept of Deep Neural Networks and go through the concepts like boolean gates, artificial neurons, Rosenblatt Neuron Perceptron, and artificial neural networks and their mechanism in detail with relevant demo and code examples.

 

Eager to dive deeper into the Machine Learning field? Great Learning offers Best Artificial Intelligence and Machine Learning Courses that are highly valued by our learners. Enroll in the program of your interest and earn a certificate of course completion that validates your industrial skills. 

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Course Outline

What is Deep Learning?

This module introduces you to the term “Deep Learning”, and you will go through its definition and an example to get an overview. 

Where DL Fits and Where to Use DL?

Through this module, you will get a clear idea of where DL belongs and how Deep Learning, Machine Learning, and Artificial Intelligence are interconnected. You will then go through Deep Learning applications to understand the usage of DL across various domains.
 

Brief History

This module articulates Deep Learning and discusses its history since the technology emerged. 

 

Why second wave?

This module focuses on needing a second wave to bring out the output. You will understand the role the second wave plays in Deep Learning. 
 

 

ML vs. DL

Deep Learning is the subset of Machine Learning, and through this module, you will understand the fine line between Machine Learning and Deep Learning. In order to help you understand it better, you will go through a car classification example.
 

Artificial Neural Network Introduction

This module will introduce you to the major concept called an artificial neural network, which plays a significant role in Deep Learning. You will go through the basic structure and function of these neural networks.
 

Tensorflow Playground Demo

In order to enhance your understanding of the mechanism of Deep Learning or a neural net model, this module puts forth a TensorFlow Playground demo.

 

Deep Learning Fundamentals

This module will walk you through Deep Learning concepts like artificial neural networks, activation functions, back propagation, and feed forward nets.
 

 

Basic Set of Layers

This module will let you comprehend the role of the basic set of layers in Deep Learning. You will go through Dense Layer, Dropout Layer, Convolution 1D, Convolution 2D, MaxPooling 1D, and LSTM in detail.
 

Activation Function

This module digs deeper into the activation function and elaborates on linear and non-linear methods of using activation functions.
 

 

Demo for Neural Network

This module contains hands-on sessions on the implementation of neural networks.

CNN Introduction

This module discusses CNN in-depth. You will be introduced to a convolutional neural network and convolutional operations, thoroughly understand its mechanism, and go through ReLu and Max pooling with examples.
 

 

RNN & LSTM

This module starts by introducing you to Recurrent Networks. You will learn about feed forward networks and recurrency. You will also go through RNN and LSTM diagrammatic representation with a thorough explanation. Lastly, you will comprehend long short-term memory.
 

 

Types of Chatbots & Conventional Interfaces

This module begins with introducing you to various use cases of types of chatbots. You will go through a diagrammatic explanation of the chatbot conversation framework and understand its role. Lastly, you will go through the conversational interfaces of chatbots.
 

Demo for CNN

This module contains an in-depth demo on CNN where you will learn its implementation through Python jupyter and understand CNN better with real-world examples.
 

Deep Neural Network Overview

This module introduces you to deep neural networks as a supervised learning method. You will go through an overview of the significant concepts you must be familiar with to understand deep neural networks better.

 

Introduction to Deep Neural Networks

In this module, first, you will learn artificial neural networks to understand deep neural networks better. You will then focus on artificial neurons and their mechanism through diagrammatic representation.
 

Boolean Gate and Artificial Neuron

This module discusses the boolean gates and helps you comprehend how they are effectively utilized to analyze the working of artificial neurons.
 

Rosenblatt Neuron Perceptron

This module helps you understand the Rosenblatt neuron perceptron model and its functions. You will also go through its implementation using Python and understand the algorithm that plays the major role of its functions in detail through code examples.
 

Artificial Neural Network

This module will help you understand artificial neural network better with the help of the diagram of the Bias Layer. You will learn about a fully connected artificial neural network and the layers involved in it and go through the mathematical foundations for artificial neural networks. 
 

 

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Introduction to Deep Learning

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