Deep Learning AZ™ 2023: Neural Networks/ AI & ChatGPT Bonus
About Course
Learn Deep Learning from scratch with this comprehensive course. This free course covers everything you need to know about deep learning, from the basics to advanced concepts. You’ll learn how to build neural networks, convolutional neural networks, recurrent neural networks, and more. You’ll also get handson experience with realworld datasets, including customer churn prediction, image recognition, stock price prediction, fraud detection, and recommender systems. This course is from Udemy, Udacity, Coursera, MasterClass, NearPeer, and some other platforms. It includes the following topics:
 Artificial Neural Networks
 Convolutional Neural Networks
 Recurrent Neural Networks
 SelfOrganizing Maps
 Boltzmann Machines
 Stacked Autoencoders
This course is completely free of cost.
This course is for anyone who wants to learn deep learning, whether you are a beginner or have some experience. You will learn the most uptodate deep learning techniques and tools, including Tensorflow, Pytorch, Theano, Keras, Scikitlearn, Numpy, Matplotlib, and Pandas.
Enroll now and start your journey into the world of deep learning!
What Will You Learn?
 Understand the intuition behind Artificial Neural Networks
 Apply Artificial Neural Networks in practice
 Understand the intuition behind Convolutional Neural Networks
 Apply Convolutional Neural Networks in practice
 Understand the intuition behind Recurrent Neural Networks
 Apply Recurrent Neural Networks in practice
 Understand the intuition behind SelfOrganizing Maps
 Apply SelfOrganizing Maps in practice
 Understand the intuition behind Boltzmann Machines
 Apply Boltzmann Machines in practice
 Understand the intuition behind AutoEncoders
 Apply AutoEncoders in practice
Course Content
Welcome to the course

A Message from the Professor

What is Deep Learning
12:34 
Updates on Udemy Reviews
01:09
ANN Intuition

Plan of Attack
02:52 
The Neuron
16:15 
The Activation Function
08:29 
How do Neural Networks work
12:48 
How do Neural Networks learn
12:59 
Gradient Descent
10:13 
Stochastic Gradient Descent
08:44 
Backpropagation
05:22
Building an ANN

Business Problem Description
04:59 
Building an ANN – Step 1
10:21 
Building an ANN – Step 2
18:36 
Building an ANN – Step 3
14:28 
Building an ANN – Step 4
11:58 
Building an ANN – Step 5
16:25 
Building an ANN – Step 5
16:25
CNN Intuition

Plan of attack
03:31 
What are convolutional neural networks
15:49 
Step 1 – Convolution Operation
16:38 
Step 1(b) – ReLU Layer
06:41 
Step 2 – Pooling
14:13 
Step 3 – Flattening
01:52 
Step 4 – Full Connection
19:25 
Summary
04:19 
Softmax & CrossEntropy
18:20
Building a CNN

Building a CNN – Step 1
11:35 
Building a CNN – Step 2
17:46 
Building a CNN – Step 3
17:56 
Building a CNN – Step 4
07:21 
Building a CNN – Step 5
14:55 
Building a CNN – FINAL DEMO!
23:38
RNN Intuition

Plan of attack
02:32 
The idea behind Recurrent Neural Networks
16:02 
The Vanishing Gradient Problem
14:27 
LSTMs
19:47 
Practical intuition
15:11 
EXTRA LSTM Variations
03:37
Building a RNN

Building a RNN – Step 1
06:29 
Building a RNN – Step 2
07:04 
Building a RNN – Step 3
05:57 
Building a RNN – Step 4
14:23 
Building a RNN – Step 5
10:40 
Building a RNN – Step 6
02:50 
Building a RNN – Step 7
08:42 
Building a RNN – Step 8
05:20 
Building a RNN – Step 9
03:20 
Building a RNN – Step 10
04:21 
Building a RNN – Step 11
10:31 
Building a RNN – Step 12
05:22 
Building a RNN – Step 13
16:50 
Building a RNN – Step 14
08:15 
Building a RNN – Step 15
09:36
SOMs Intuition

Plan of attack
03:10 
How do SelfOrganizing Maps Work
08:30 
Why revisit KMeans
02:19 
KMeans Clustering (Refresher)
14:17 
How do SelfOrganizing Maps Learn (Part 1)
14:24 
How do SelfOrganizing Maps Learn (Part 2)
09:37 
Live SOM example
04:28 
Reading an Advanced SOM
14:26 
EXTRA Kmeans Clustering (part 2)
07:48 
EXTRA Kmeans Clustering (part 3)
11:51
Building a SOM

How to get the dataset
01:32 
Building a SOM – Step 1
13:42 
Building a SOM – Step 1
13:42 
Building a SOM – Step 2
09:39 
Building a SOM – Step 3
17:02 
Building a SOM – Step 4
11:12
Mega Case Study

Mega Case Study – Step 1
02:49 
Mega Case Study – Step 2
04:16 
Mega Case Study – Step 3
14:37 
Mega Case Study – Step 4
09:02
———————— Part 5 – Boltzmann Machines ————————

Plan of attack
02:24
Boltzmann Machine Intuition

Boltzmann Machine
14:22 
EnergyBased Models (EBM)
10:39 
Editing Wikipedia – Our Contribution to the World
03:28 
Restricted Boltzmann Machine
17:29 
Contrastive Divergence
16:28 
Deep Belief Networks
05:23 
Deep Boltzmann Machines
02:57
Building a Boltzmann Machine

Building a Boltzmann Machine – Introduction
09:09 
Building a Boltzmann Machine – Step 1
09:13 
Building a Boltzmann Machine – Step 2
09:40 
Building a Boltzmann Machine – Step 3
08:21 
Building a Boltzmann Machine – Step 4
20:53 
Building a Boltzmann Machine – Step 5
05:05 
Building a Boltzmann Machine – Step 6
07:33 
Building a Boltzmann Machine – Step 7
10:13 
Building a Boltzmann Machine – Step 8
12:36 
Building a Boltzmann Machine – Step 9
06:17 
Building a Boltzmann Machine – Step 10
11:34 
Building a Boltzmann Machine – Step 11
06:57 
Building a Boltzmann Machine – Step 12
13:23 
Building a Boltzmann Machine – Step 13
18:42 
Building a Boltzmann Machine – Step 14
17:10
————————— Part 6 – AutoEncoders —————————

Plan of attack
02:12
AutoEncoders Intuition

Auto Encoders
10:50 
A Note on Biases
01:15 
Training an Auto Encoder
06:10 
Overcomplete hidden layers
03:52 
Sparse Autoencoders
06:15 
Denoising Autoencoders
02:32 
Contractive Autoencoders
02:23 
Stacked Autoencoders
01:54 
Deep Autoencoders
01:50
Building an AutoEncoder

How to get the dataset
01:32 
Building an AutoEncoder – Step 1
12:04 
Building an AutoEncoder – Step 2
11:49 
Building an AutoEncoder – Step 3
08:21 
Building an AutoEncoder – Step 4
20:51 
Building an AutoEncoder – Step 5
05:04 
Building an AutoEncoder – Step 6
16:45 
Building an AutoEncoder – Step 7
13:37 
Building an AutoEncoder – Step 8
15:05 
Building an AutoEncoder – Step 9
13:32 
Building an AutoEncoder – Step 10
04:22 
Building an AutoEncoder – Step 11
11:26 
THANK YOU bonus video
02:40
Regression & Classification Intuition

Simple Linear Regression Intuition – Step 1
05:45 
Simple Linear Regression Intuition – Step 2
03:09 
Multiple Linear Regression Intuition
01:03 
Logistic Regression Intuition
17:07
Data Preprocessing Template

Data Preprocessing – Step 1
10:50 
Data Preprocessing – Step 2
03:34 
Data Preprocessing – Step 3
15:42 
Data Preprocessing – Step 4
12:15 
Data Preprocessing – Step 5
14:58 
Data Preprocessing – Step 6
13:47 
Data Preprocessing – Step 7
20:31
Logistic Regression Implementation

Logistic Regression – Step 1
09:43 
Logistic Regression – Step 2
13:38 
Logistic Regression – Step 3
07:40 
Logistic Regression – Step 4
07:49 
Logistic Regression – Step 5
06:15 
Logistic Regression – Step 6
09:26 
Logistic Regression – Step 7
16:06
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