Deep Learning A-Z™ 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 hands-on experience with real-world 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
- Self-Organizing 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 up-to-date deep learning techniques and tools, including Tensorflow, Pytorch, Theano, Keras, Scikit-learn, 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 Self-Organizing Maps
- Apply Self-Organizing 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
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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 & Cross-Entropy
18:20
Building a CNN
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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
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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 Self-Organizing Maps Work
08:30 -
Why revisit K-Means
02:19 -
K-Means Clustering (Refresher)
14:17 -
How do Self-Organizing Maps Learn (Part 1)
14:24 -
How do Self-Organizing Maps Learn (Part 2)
09:37 -
Live SOM example
04:28 -
Reading an Advanced SOM
14:26 -
EXTRA K-means Clustering (part 2)
07:48 -
EXTRA K-means 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 -
Energy-Based 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
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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
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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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