- 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!
Deep Learning A-Z 2024: Neural Networks, AI & ChatGPT Prize
Learn how to develop deep learning algorithms using Python by gaining a practical understanding of artificial neural ...Read more
Online
₹ 4099
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.
The highlights
- Certificate of completion
- Self-paced course
- 22.5 hours of pre-recorded video content
- 38 articles
- 5 downloadable resources
Program offerings
- Online course
- Learning resources. 30-day money-back guarantee
- Unlimited access
- Accessible on mobile devices and tv
Course and certificate fees
Fees information
certificate availability
Yes
certificate providing authority
Udemy
Who it is for
What you will learn
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
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***