Course Content
Subtitle Guide – Hướng dẫn thêm phụ đề
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01 – Welcome to the course!
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002 What is Deep Learning_part1
06:17
002 What is Deep Learning_part2
06:17
02 – Part 1 – Artificial Neural Networks
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03 – ANN Intuition
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002 Plan of Attack
02:52
003 The Neuron
16:15
004 The Activation Function
08:29
005 How do Neural Networks work
12:47
006 How do Neural Networks learn
12:59
007 Gradient Descent
10:13
008 Stochastic Gradient Descent
08:44
009 Backpropagation
05:22
04 – Building an ANN
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001 Business Problem Description
04:59
003 Building an ANN – Step 1
10:21
005 Building an ANN – Step 2_part1
09:18
005 Building an ANN – Step 2_part2
09:18
006 Building an ANN – Step 3
14:28
007 Building an ANN – Step 4
11:58
008 Building an ANN – Step 5_part1
08:12
008 Building an ANN – Step 5_part2
08:12
05 Part 2 – Convolutional Neural Networks
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06 – CNN Intuition
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002 Plan of attack
03:31
003 What are convolutional neural networks
15:49
004 Step 1 – Convolution Operation
16:38
005 Step 1(b) – ReLU Layer
06:41
006 Step 2 – Pooling_part1
07:06
006 Step 2 – Pooling_part2_part1
03:33
006 Step 2 – Pooling_part2_part2
03:33
007 Step 3 – Flattening
01:52
008 Step 4 – Full Connection_part1
09:42
008 Step 4 – Full Connection_part2
09:42
009 Summary
04:19
010 Softmax & Cross-Entropy
18:20
07 – Building a CNN
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002 Building a CNN – Step 1
11:35
003 Building a CNN – Step 2_part1
08:53
003 Building a CNN – Step 2_part2
08:53
004 Building a CNN – Step 3_part1
08:58
004 Building a CNN – Step 3_part2
08:58
005 Building a CNN – Step 4
07:21
006 Building a CNN – Step 5_part1
07:27
006 Building a CNN – Step 5_part2
07:27
008 Building a CNN – FINAL DEMO!_part1
11:49
008 Building a CNN – FINAL DEMO!_part2_part1
05:54
008 Building a CNN – FINAL DEMO!_part2_part2
05:54
08 – Part 3 – Recurrent Neural Networks ———————-
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09 – RNN Intuition
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002 Plan of attack
02:32
003 The idea behind Recurrent Neural Networks_part1
08:01
003 The idea behind Recurrent Neural Networks_part2
08:00
004 The Vanishing Gradient Problem
14:27
005 LSTMs
19:47
006 Practical intuition_part1
05:03
006 Practical intuition_part2
05:03
006 Practical intuition_part3
05:03
007 EXTRA LSTM Variations
03:37
10 – Building a RNN
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002 Building a RNN – Step 1
06:29
003 Building a RNN – Step 2
07:04
004 Building a RNN – Step 3
05:57
005 Building a RNN – Step 4_part1
07:11
005 Building a RNN – Step 4_part2
07:11
006 Building a RNN – Step 5_part1
05:20
006 Building a RNN – Step 5_part2
05:20
007 Building a RNN – Step 6
02:50
008 Building a RNN – Step 7
08:42
009 Building a RNN – Step 8
05:20
010 Building a RNN – Step 9
03:20
011 Building a RNN – Step 10
04:21
012 Building a RNN – Step 11
10:31
013 Building a RNN – Step 12
05:22
014 Building a RNN – Step 13_part1
05:36
014 Building a RNN – Step 13_part2
05:36
014 Building a RNN – Step 13_part3
05:36
015 Building a RNN – Step 14_part1
04:07
015 Building a RNN – Step 14_part2
04:07
016 Building a RNN – Step 15_part1
04:48
016 Building a RNN – Step 15_part2
04:48
11 – Evaluating and Improving the RNN
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12 -Part 4 – Self Organizing Maps
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13 – SOMs Intuition
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001 Plan of attack
03:10
002 How do Self-Organizing Maps Work
08:30
003 Why revisit K-Means
02:19
004 K-Means Clustering (Refresher)
14:17
005 How do Self-Organizing Maps Learn (Part 1)_part1
07:12
005 How do Self-Organizing Maps Learn (Part 1)_part2
07:12
006 How do Self-Organizing Maps Learn (Part 2)
09:37
007 Live SOM example
04:28
008 Reading an Advanced SOM_part1
04:48
008 Reading an Advanced SOM_part2
04:48
008 Reading an Advanced SOM_part3
04:48
009 EXTRA K-means Clustering (part 2)
07:48
010 EXTRA K-means Clustering (part 3)
11:51
14 – Building a SOM
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001 How to get the dataset
01:32
002 Building a SOM – Step 1
13:42
003 Building a SOM – Step 2
09:39
004 Building a SOM – Step 3
17:25
005 Building a SOM – Step 4
11:12
15 – Mega Case Study
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001 Mega Case Study – Step 1
02:49
002 Mega Case Study – Step 2
04:16
003 Mega Case Study – Step 3_part1
07:18
003 Mega Case Study – Step 3_part2
07:18
004 Mega Case Study – Step 4
09:02
16 -Part 5 – Boltzmann Machines
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17 – Boltzmann Machine Intuition
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001 Plan of attack
02:24
002 Boltzmann Machine_part1
07:11
002 Boltzmann Machine_part2
07:11
003 Energy-Based Models (EBM)
10:39
004 Editing Wikipedia – Our Contribution to the World
03:28
005 Restricted Boltzmann Machine_part1
08:44
005 Restricted Boltzmann Machine_part2
08:44
006 Contrastive Divergence
16:29
007 Deep Belief Networks
05:24
008 Deep Boltzmann Machines
02:57
18 – Building a Boltzmann Machine
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001 How to get the dataset
01:32
003 Building a Boltzmann Machine – Introduction
09:09
005 Building a Boltzmann Machine – Step 1_part1
04:36
005 Building a Boltzmann Machine – Step 1_part2
04:36
006 Building a Boltzmann Machine – Step 2
09:40
007 Building a Boltzmann Machine – Step 3
08:21
008 Building a Boltzmann Machine – Step 4_part1
10:26
008 Building a Boltzmann Machine – Step 4_part2
10:26
009 Building a Boltzmann Machine – Step 5
05:05
010 Building a Boltzmann Machine – Step 6_part1
03:46
010 Building a Boltzmann Machine – Step 6_part2
03:46
011 Building a Boltzmann Machine – Step 7
10:13
012 Building a Boltzmann Machine – Step 8
12:36
013 Building a Boltzmann Machine – Step 9
06:17
014 Building a Boltzmann Machine – Step 10_part1
05:47
014 Building a Boltzmann Machine – Step 10_part2
05:47
015 Building a Boltzmann Machine – Step 11
06:57
016 Building a Boltzmann Machine – Step 12_part1
06:41
016 Building a Boltzmann Machine – Step 12_part2
06:41
017 Building a Boltzmann Machine – Step 13_part1
06:14
017 Building a Boltzmann Machine – Step 13_part2
06:14
017 Building a Boltzmann Machine – Step 13_part3
06:14
018 Building a Boltzmann Machine – Step 14_part1
08:35
018 Building a Boltzmann Machine – Step 14_part2
08:35
19 -Part 6 – AutoEncoders
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20 – AutoEncoders Intuition
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001 Plan of attack
02:12
002 Auto Encoders_part1
05:25
002 Auto Encoders_part2
05:25
003 A Note on Biases
01:15
004 Training an Auto Encoder
06:10
005 Overcomplete hidden layers
03:52
006 Sparse Autoencoders
06:15
007 Denoising Autoencoders
02:32
008 Contractive Autoencoders
02:23
009 Stacked Autoencoders
01:54
010 Deep Autoencoders
01:50
21 – Building an AutoEncoder
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001 How to get the dataset
01:32
004 Building an AutoEncoder – Step 1_part1
06:02
004 Building an AutoEncoder – Step 1_part2
06:02
005 Building an AutoEncoder – Step 2
11:49
006 Building an AutoEncoder – Step 3
08:21
008 Building an AutoEncoder – Step 4_part1
10:25
008 Building an AutoEncoder – Step 4_part2
10:25
009 Building an AutoEncoder – Step 5
05:04
010 Building an AutoEncoder – Step 6_part1
08:22
010 Building an AutoEncoder – Step 6_part2
08:22
011 Building an AutoEncoder – Step 7_part1
06:48
011 Building an AutoEncoder – Step 7_part2
06:48
012 Building an AutoEncoder – Step 8_part1
07:32
012 Building an AutoEncoder – Step 8_part2
07:32
013 Building an AutoEncoder – Step 9
13:32
014 Building an AutoEncoder – Step 10
04:22
015 Building an AutoEncoder – Step 11
11:26
016 THANK YOU Video_part1
01:20
016 THANK YOU Video_part2
01:20
22 – Annex – Get the Machine Learning Basics
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23 – Regression & Classification Intuition
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002 Simple Linear Regression Intuition – Step 1
05:45
003 Simple Linear Regression Intuition – Step 2
03:09
004 Multiple Linear Regression Intuition
01:03
005 Logistic Regression Intuition
17:06
24 – Data Preprocessing
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002 The Machine Learning process
01:31
003 Splitting the data into a Training and Test set
02:02
004 Feature Scaling
06:27
25 – Data Preprocessing in Python
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001 Getting Started – Step 1
05:21
002 Getting Started – Step 2
05:21
003 Importing the Libraries
03:34
004 Importing the Dataset – Step 1
05:13
005 Importing the Dataset – Step 2
04:42
006 Importing the Dataset – Step 3
05:46
008 Taking care of Missing Data – Step 1
05:56
009 Taking care of Missing Data – Step 2
05:58
010 Encoding Categorical Data – Step 1
04:24
011 Encoding Categorical Data – Step 2
05:54
012 Encoding Categorical Data – Step 3
04:39
013 Splitting the dataset into the Training set and Test set – Step 1
03:55
014 Splitting the dataset into the Training set and Test set – Step 2
05:59
015 Splitting the dataset into the Training set and Test set – Step 3
03:52
016 Feature Scaling – Step 1
05:56
017 Feature Scaling – Step 2
04:45
018 Feature Scaling – Step 3
03:48
019 Feature Scaling – Step 4
05:59
26 – Logistic Regression
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001 Logistic Regression Intuition
04:55
002 Maximum Likelihood
03:50
003 Logistic Regression in Python – Step 1a
05:43
004 Logistic Regression in Python – Step 1b
03:59
005 Logistic Regression in Python – Step 2a
05:51
006 Logistic Regression in Python – Step 2b
05:57
007 Logistic Regression in Python – Step 3a
03:58
008 Logistic Regression in Python – Step 3b
03:30
009 Logistic Regression in Python – Step 4a
05:59
010 Logistic Regression in Python – Step 4b
01:49
011 Logistic Regression in Python – Step 5
05:59
012 Logistic Regression in Python – Step 6a
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013 Logistic Regression in Python – Step 6b
03:33
014 Logistic Regression in Python – Step 7a
05:54
015 Logistic Regression in Python – Step 7b
03:44
016 Logistic Regression in Python – Step 7c
03:19
27 – Congratulations!! Don’t forget your Prize )
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Deep Learning A-Z 2024: Neural Networks, AI & ChatGPT Prize
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