Quick Facts

particular	details
Medium of instructions English	Mode of learning Self study	Mode of Delivery Video and Text Based

Course overview

A unique class of machine learning algorithms called artificial neural networks is based on how the human brain processes. ANN can learn from the data and produce outputs in the manner of forecasts or categorization, much like the neurons in our nervous system can do. The Deep Learning A-Z 2024: Neural Networks, AI & ChatGPT Prize online certification was created by data scientist Kirill Eremenko, AI entrepreneur Hadelin de Ponteves, and the Ligency Team, which is available on Udemy.

Deep Learning A-Z 2024: Neural Networks, AI & ChatGPT Prize online course is intended for learners interested in understanding the more advanced principles of deep learning and machine learning, including logistic regression and linear regression. With Deep Learning A-Z™: Hands-On Artificial Neural Networks online classes, learners will be provided with 22.5 hours of videos along with 38 articles and 5 downloadable resources which explain the functionalities of tools like TensorFlow and PyTorch as well as discuss the strategies involved with ANN intuition, CNN intuition, RNN intuition, and SOM intuition.

Course and certificate fees

Fees information

₹ 4,099

certificate availability

Yes

certificate providing authority

Udemy

Who it is for

It engineer Ai developer Machine learning engineer Data scientist Data engineer Entrepreneur

What you will learn

Knowledge of deep learning

After completing the Deep Learning A-Z 2024: Neural Networks, AI & ChatGPT Prize certification course, learners will gain a hands-on experience in the fundamentals of deep learning and artificial neural networks. Learners will explore the functions of convolutional neural networks and recurrent neural networks. Learners will study ANN intuition, CNN intuition, RNN intuition, and SOM intuition as well as will acquire the knowledge of the strategies associated with linear regression, logistic regression, and data processing. Learners will also study about features of Boltzmann machines, AutoEncoders, and self-organizing maps.

The syllabus

Welcome to the course

What is Deep Learning?
Updates on Udemy Reviews
BONUS: Learning Paths
BONUS: Meet Your Instructors
Some Additional Resources!!
FAQBot!
Get the materials
Your Shortcut To Becoming A Better Data Scientist!

Part 1 - Artificial Neural Networks

ANN Intuition

What You'll Need for ANN
Plan of Attack
The Neuron
The Activation Function
How do Neural Networks work?
How do Neural Networks learn?
Gradient Descent
Stochastic Gradient Descent
Backpropagation

Building an ANN

Business Problem Description
IMPORTANT NOTE
Building an ANN - Step 1
Check out our free course on ANN for Regression
Building an ANN - Step 2
Building an ANN - Step 3
Building an ANN - Step 4
Building an ANN - Step 5

Part 2 - Convolutional Neural Networks

CNN Intuition

What You'll Need for CNN
Plan of attack
What are convolutional neural networks?
Step 1 - Convolution Operation
Step 1(b) - ReLU Layer
Step 2 - Pooling
Step 3 - Flattening
Step 4 - Full Connection
Summary
Softmax & Cross-Entropy

Building a CNN

IMPORTANT NOTE
Building a CNN - Step 1
Building a CNN - Step 2
Building a CNN - Step 3
Building a CNN - Step 4
Building a CNN - Step 5
Quick Note
Building a CNN - FINAL DEMO!

Part 3 - Recurrent Neural Networks

RNN Intuition

What You'll Need for RNN
Plan of attack
The idea behind Recurrent Neural Networks
The Vanishing Gradient Problem
LSTMs
Practical intuition
EXTRA: LSTM Variations

Building a RNN

IMPORTANT NOTE
Building a RNN - Step 1
Building a RNN - Step 2
Building a RNN - Step 3
Building a RNN - Step 4
Building a RNN - Step 5
Building a RNN - Step 6
Building a RNN - Step 7
Building a RNN - Step 8
Building a RNN - Step 9
Building a RNN - Step 10
Building a RNN - Step 11
Building a RNN - Step 12
Building a RNN - Step 13
Building a RNN - Step 14
Building a RNN - Step 15

Evaluating and Improving the RNN

Evaluating the RNN
Improving the RNN

Part 4 - Self Organizing Maps

SOMs Intuition

Plan of attack
How do Self-Organizing Maps Work?
Why revisit K-Means?
K-Means Clustering (Refresher)
How do Self-Organizing Maps Learn? (Part 1)
How do Self-Organizing Maps Learn? (Part 2)
Live SOM example
Reading an Advanced SOM
EXTRA: K-means Clustering (part 2)
EXTRA: K-means Clustering (part 3)

Building a SOM

IMPORTANT NOTE
How to get the dataset
Building a SOM - Step 1
Building a SOM - Step 2
Building a SOM - Step 3
Building a SOM - Step 4

Mega Case Study

IMPORTANT NOTE
Mega Case Study - Step 1
Mega Case Study - Step 2
Mega Case Study - Step 3
Mega Case Study - Step 4

Part 5 - Boltzmann Machines

Boltzmann Machine Intuition

Boltzmann Machine
Energy-Based Models (EBM)
Editing Wikipedia - Our Contribution to the World
Restricted Boltzmann Machine
Contrastive Divergence
Deep Belief Networks
Deep Boltzmann Machines
How to get the dataset

Building a Boltzmann Machine

IMPORTANT NOTE
Installing PyTorch
Building a Boltzmann Machine - Introduction
Same Data Preprocessing in Parts 5 and 6
Building a Boltzmann Machine - Step 1
Building a Boltzmann Machine - Step 2
Building a Boltzmann Machine - Step 3
Building a Boltzmann Machine - Step 4
Building a Boltzmann Machine - Step 5
Building a Boltzmann Machine - Step 6
Building a Boltzmann Machine - Step 7
Building a Boltzmann Machine - Step 8
Building a Boltzmann Machine - Step 9
Building a Boltzmann Machine - Step 10
Building a Boltzmann Machine - Step 11
Building a Boltzmann Machine - Step 12
Building a Boltzmann Machine - Step 13
Building a Boltzmann Machine - Step 14
Evaluating the Boltzmann Machine

Part 6 - AutoEncoders

AutoEncoders Intuition

Auto Encoders
A Note on Biases
Training an Auto Encoder
Overcomplete hidden layers
Sparse Autoencoders
Denoising Autoencoders
Contractive Autoencoders
Stacked Autoencoders
Deep Autoencoders

Building an AutoEncoder

IMPORTANT NOTE
How to get the dataset
Installing PyTorch
Same Data Preprocessing in Parts 5 and 6
Building an AutoEncoder - Step 1
Building an AutoEncoder - Step 2
Building an AutoEncoder - Step 3
Homework Challenge - Coding Exercise
Building an AutoEncoder - Step 4
Building an AutoEncoder - Step 5
Building an AutoEncoder - Step 6
Building an AutoEncoder - Step 7
Building an AutoEncoder - Step 8
Building an AutoEncoder - Step 9
Building an AutoEncoder - Step 10
Building an AutoEncoder - Step 11
THANK YOU bonus video

Annex - Get the Machine Learning Basics

Regression & Classification Intuition

What You Need for Regression & Classification
Simple Linear Regression Intuition - Step 1
Simple Linear Regression Intuition - Step 2
Multiple Linear Regression Intuition
Logistic Regression Intuition

Data Preprocessing Template

Important Instructions
Data Preprocessing - Step 1
Data Preprocessing - Step 2
Data Preprocessing - Step 3
Data Preprocessing - Step 4
Data Preprocessing - Step 5
Data Preprocessing - Step 6
Data Preprocessing - Step 7

Logistic Regression Implementation

Important Instructions
Logistic Regression - Step 1
Logistic Regression - Step 2
Logistic Regression - Step 3
Logistic Regression - Step 4
Logistic Regression - Step 5
Logistic Regression - Step 6
Logistic Regression - Step 7

Bonus Lectures

***YOUR SPECIAL BONUS***

Instructors

Mr Kirill Eremenko
Data Scientist
Udemy

Mr Hadelin de Ponteves
Co-founder
Udemy

Articles

Latest Articles

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