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Introduction to Deep Learning

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

4.51
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Beginner

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5.25 Hrs

Learning hours

4.7K+

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Skills you’ll Learn

About this Course

This online Introduction to 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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4.51
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Ratings & Reviews of this Course

Explored Neural Networks, Machine Learning Techniques, and Practical Coding, Deepening My Understanding of AI and Programming.
This program helped me to enhance my programming skills and my knowledge of deep learning.
Reviewer Profile

4.0

The Course is Easy to Follow. The Topics Have Enough Depth.
The course is easy to follow. The topics have enough depth. It increased my knowledge and skills.

Introduction to Deep Learning

4.51
average rating

Ratings

5.25 Hrs

Learning hours

Beginner

Level

4.7K+

Learners

10 Million+ learners

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Frequently Asked Questions

What prerequisites are required to learn this Deep Learning free course?

There are no prerequisites required to enroll in this online Deep Learning free course. It is specifically designed for beginners to learn concepts from scratch.
 

How long does it take to complete this free Deep Learning course?

This free Introduction to Deep Learning course contains 3.5 hours of self-paced videos that learners can take up according to their convenience.

 

Will I have lifetime access to this free online course?

Yes. You will have lifetime access to this free online Deep Learning course.

What are my next learning options after this Deep Learning course?

You can enroll in Great Learning’s Data Science and Machine Learning PG Course By MIT to gain advanced knowledge regarding Machine Learning and earn a certificate of course completion.

Is it worth learning Deep Learning?

Yes, Deep Learning is worth learning because it can be used to achieve state-of-the-art performance in many artificial intelligence tasks, such as image classification, object detection, and language translation.
 

What is Deep Learning used for?

Deep Learning is used for various tasks, including but not limited to image recognition, natural language processing, computer vision, time series forecasting, and recommendation systems.
 

Why is Deep Learning so popular?

Deep learning is so popular because it is a powerful tool for solving problems, and it has the ability to learn from data and find patterns that humans might not be able to see. The significant reasons for its increasing popularity include its scalability, efficiency, availability, and highly effective methods for solving complex problems.

What jobs demand that you learn Deep Learning?

Jobs that commonly demand Deep Learning skills include:

  • Data Scientist
  • Machine Learning Engineer
  • Research Scientist
  • Artificial Intelligence Engineer

Will I get a certificate after completing this Introduction to Deep Learning course?

Yes, you will be rewarded with a free Deep Learning Certificate of course completion after completing all the modules and the quiz at the end of this free Deep Learning course.
 

What knowledge and skills will I gain upon completing this Deep Learning course?

By the end of this Introduction to Deep Learning free course, you will have a brief knowledge of it. You will learn various critical Deep Learning concepts and terms like artificial neural networks, CNN, deep neural networks, and more. You will go through various relevant Deep Learning examples and understand its different frameworks. Through a thorough understanding of these concepts, you will be able to comprehend the Deep Learning applications in real-world problems.
 

How much does this Deep Learning course cost?

This Introduction to Deep Learning course is offered for free by Great Learning Academy.
 

Is there a limit on how many times I can take this Deep Learning course?

No, there are no such limits on the number of times you can attain this Deep Learning free course.
 

Can I sign up for multiple courses from Great Learning Academy at the same time?

Yes, you can sign up for more than one free course offered by Great Learning Academy that efficiently helps your career growth.
 

Why choose Great Learning for this Introduction to Deep Learning course?

Great Learning Academy is an initiative taken by the leading e-learning platform, Great Learning. Great Learning Academy provides you with industry-relevant courses for free, and Introduction to Deep Learning is one of the free courses that empowers you with in-demand Deep Learning skills.

 

Who is eligible to take this Deep Learning free course?

Any beginner who wants to get acquainted with Deep Learning and learn the concepts from the basic to intermediate level can enroll in this free Introduction to Deep Learning course.
 

What are the steps to enroll in this course?

 

  • Search for the “Introduction to Deep Learning” free course in the search bar present at the top corner of Great Learning Academy.
  • Register for the course through the Enroll Now button and start learning.

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