Course Content
15 – Mega Case Study
0/4
001 Mega Case Study – Step 1.mp4
02:49
002 Mega Case Study – Step 2.mp4
04:16
003 Mega Case Study – Step 3.mp4
14:37
004 Mega Case Study – Step 4.mp4
09:02
26 – Logistic Regression
0/16
001 Logistic Regression Intuition.mp4
04:55
002 Maximum Likelihood.mp4
03:50
003 Logistic Regression in Python – Step 1a.mp4
05:42
004 Logistic Regression in Python – Step 1b.mp4
03:58
005 Logistic Regression in Python – Step 2a.mp4
05:50
006 Logistic Regression in Python – Step 2b.mp4
05:56
007 Logistic Regression in Python – Step 3a.mp4
03:58
008 Logistic Regression in Python – Step 3b.mp4
03:29
009 Logistic Regression in Python – Step 4a.mp4
05:58
010 Logistic Regression in Python – Step 4b.mp4
01:48
011 Logistic Regression in Python – Step 5.mp4
05:58
012 Logistic Regression in Python – Step 6a.mp4
05:52
013 Logistic Regression in Python – Step 6b.mp4
03:32
014 Logistic Regression in Python – Step 7a.mp4
05:53
015 Logistic Regression in Python – Step 7b.mp4
03:44
016 Logistic Regression in Python – Step 7c.mp4
03:18
25 – Data Preprocessing in Python
0/18
011 Encoding Categorical Data – Step 2.mp4
05:53
019 Feature Scaling – Step 4.mp4
05:58
018 Feature Scaling – Step 3.mp4
03:47
017 Feature Scaling – Step 2.mp4
04:44
016 Feature Scaling – Step 1.mp4
05:56
015 Splitting the dataset into the Training set and Test set – Step 3.mp4
03:51
014 Splitting the dataset into the Training set and Test set – Step 2.mp4
05:58
013 Splitting the dataset into the Training set and Test set – Step 1.mp4
03:54
012 Encoding Categorical Data – Step 3.mp4
04:39
001 Getting Started – Step 1.mp4
05:21
010 Encoding Categorical Data – Step 1.mp4
04:24
009 Taking care of Missing Data – Step 2.mp4
05:58
008 Taking care of Missing Data – Step 1.mp4
05:55
006 Importing the Dataset – Step 3.mp4
05:45
005 Importing the Dataset – Step 2.mp4
04:42
004 Importing the Dataset – Step 1.mp4
05:13
003 Importing the Libraries.mp4
03:33
002 Getting Started – Step 2.mp4
05:20
24 – Data Preprocessing
0/3
002 The Machine Learning process.mp4
01:31
003 Splitting the data into a Training and Test set.mp4
02:02
004 Feature Scaling.mp4
06:27
23 – Regression & Classification Intuition
0/4
002 Simple Linear Regression Intuition – Step 1.mp4
05:45
003 Simple Linear Regression Intuition – Step 2.mp4
03:09
004 Multiple Linear Regression Intuition.mp4
01:03
005 Logistic Regression Intuition.mp4
17:06
21 – Building an AutoEncoder
0/13
001 How to get the dataset.mp4
01:32
004 Building an AutoEncoder – Step 1.mp4
12:04
005 Building an AutoEncoder – Step 2.mp4
11:49
006 Building an AutoEncoder – Step 3.mp4
08:20
008 Building an AutoEncoder – Step 4.mp4
20:51
009 Building an AutoEncoder – Step 5.mp4
05:04
010 Building an AutoEncoder – Step 6.mp4
16:45
011 Building an AutoEncoder – Step 7.mp4
13:36
012 Building an AutoEncoder – Step 8.mp4
15:05
013 Building an AutoEncoder – Step 9.mp4
13:32
014 Building an AutoEncoder – Step 10.mp4
04:21
015 Building an AutoEncoder – Step 11.mp4
11:25
016 THANK YOU Video.mp4
02:40
20 – AutoEncoders Intuition
0/10
001 Plan of attack.mp4
02:12
002 Auto Encoders.mp4
10:50
003 A Note on Biases.mp4
01:15
004 Training an Auto Encoder.mp4
06:10
005 Overcomplete hidden layers.mp4
03:52
006 Sparse Autoencoders.mp4
06:15
007 Denoising Autoencoders.mp4
02:32
008 Contractive Autoencoders.mp4
02:23
009 Stacked Autoencoders.mp4
01:54
010 Deep Autoencoders.mp4
01:50
18 – Building a Boltzmann Machine
0/16
001 How to get the dataset.mp4
01:32
003 Building a Boltzmann Machine – Introduction.mp4
09:09
005 Building a Boltzmann Machine – Step 1.mp4
09:13
006 Building a Boltzmann Machine – Step 2.mp4
09:39
007 Building a Boltzmann Machine – Step 3.mp4
08:20
008 Building a Boltzmann Machine – Step 4.mp4
20:53
009 Building a Boltzmann Machine – Step 5.mp4
05:04
010 Building a Boltzmann Machine – Step 6.mp4
07:33
011 Building a Boltzmann Machine – Step 7.mp4
10:13
012 Building a Boltzmann Machine – Step 8.mp4
12:35
013 Building a Boltzmann Machine – Step 9.mp4
06:16
014 Building a Boltzmann Machine – Step 10.mp4
11:34
015 Building a Boltzmann Machine – Step 11.mp4
06:57
016 Building a Boltzmann Machine – Step 12.mp4
13:23
017 Building a Boltzmann Machine – Step 13.mp4
18:42
018 Building a Boltzmann Machine – Step 14.mp4
17:09
17 – Boltzmann Machine Intuition
0/8
001 Plan of attack.mp4
02:24
002 Boltzmann Machine.mp4
14:22
003 Energy-Based Models (EBM).mp4
10:39
004 Editing Wikipedia – Our Contribution to the World.mp4
03:28
005 Restricted Boltzmann Machine.mp4
17:29
006 Contrastive Divergence.mp4
16:28
007 Deep Belief Networks.mp4
05:23
008 Deep Boltzmann Machines.mp4
02:57
01 – Welcome to the course!
0/1
002 What is Deep Learning.mp4
12:34
14 – Building a SOM
0/5
001 How to get the dataset.mp4
01:32
002 Building a SOM – Step 1.mp4
13:41
003 Building a SOM – Step 2.mp4
09:39
004 Building a SOM – Step 3.mp4
17:24
005 Building a SOM – Step 4.mp4
11:11
13 – SOMs Intuition
0/10
001 Plan of attack.mp4
03:10
002 How do Self-Organizing Maps Work.mp4
08:30
003 Why revisit K-Means.mp4
02:19
004 K-Means Clustering (Refresher).mp4
14:17
005 How do Self-Organizing Maps Learn (Part 1).mp4
14:24
006 How do Self-Organizing Maps Learn (Part 2).mp4
09:37
007 Live SOM example.mp4
04:28
008 Reading an Advanced SOM.mp4
14:26
009 EXTRA K-means Clustering (part 2).mp4
07:48
010 EXTRA K-means Clustering (part 3).mp4
11:51
10 – Building a RNN
0/15
002 Building a RNN – Step 1.mp4
06:29
003 Building a RNN – Step 2.mp4
07:04
004 Building a RNN – Step 3.mp4
05:57
005 Building a RNN – Step 4.mp4
14:22
006 Building a RNN – Step 5.mp4
10:39
007 Building a RNN – Step 6.mp4
02:50
008 Building a RNN – Step 7.mp4
08:42
009 Building a RNN – Step 8.mp4
05:20
010 Building a RNN – Step 9.mp4
03:20
011 Building a RNN – Step 10.mp4
04:21
012 Building a RNN – Step 11.mp4
10:31
013 Building a RNN – Step 12.mp4
05:22
014 Building a RNN – Step 13.mp4
16:50
015 Building a RNN – Step 14.mp4
08:15
016 Building a RNN – Step 15.mp4
09:35
09 – RNN Intuition
0/6
002 Plan of attack.mp4
02:32
003 The idea behind Recurrent Neural Networks.mp4
16:01
004 The Vanishing Gradient Problem.mp4
14:27
005 LSTMs.mp4
19:47
006 Practical intuition.mp4
15:11
007 EXTRA LSTM Variations.mp4
03:36
07 – Building a CNN
0/6
002 Building a CNN – Step 1.mp4
11:34
003 Building a CNN – Step 2.mp4
17:46
004 Building a CNN – Step 3.mp4
17:55
005 Building a CNN – Step 4.mp4
07:21
006 Building a CNN – Step 5.mp4
14:55
008 Building a CNN – FINAL DEMO!.mp4
23:37
06 – CNN Intuition
0/9
002 Plan of attack.mp4
03:31
003 What are convolutional neural networks.mp4
15:49
004 Step 1 – Convolution Operation.mp4
16:38
005 Step 1(b) – ReLU Layer.mp4
06:41
006 Step 2 – Pooling.mp4
14:13
007 Step 3 – Flattening.mp4
01:52
008 Step 4 – Full Connection.mp4
19:24
009 Summary.mp4
04:19
010 Softmax & Cross-Entropy.mp4
18:19
04 – Building an ANN
0/6
001 Business Problem Description.mp4
04:59
003 Building an ANN – Step 1.mp4
10:20
005 Building an ANN – Step 2.mp4
18:36
006 Building an ANN – Step 3.mp4
14:27
007 Building an ANN – Step 4.mp4
11:57
008 Building an ANN – Step 5.mp4
16:24
03 – ANN Intuition
0/8
002 Plan of Attack.mp4
02:51
003 The Neuron.mp4
16:15
004 The Activation Function.mp4
08:29
005 How do Neural Networks work.mp4
12:47
006 How do Neural Networks learn.mp4
12:58
007 Gradient Descent.mp4
10:13
008 Stochastic Gradient Descent.mp4
08:44
009 Backpropagation.mp4
05:21
[Last updated 8/2024] Deep Learning A-Z 2024: Neural Networks, AI & ChatGPT Prize (Udemy – Vietsub and Engsub)
Previous
Next
0% Complete