[Last updated 8/2024] Deep Learning A-Z 2024: Neural Networks, AI & ChatGPT Prize (Udemy – Vietsub and Engsub)
About Course
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Master
Deep Learning A-Z 2024
: Dive into Neural Networks, AI, and ChatGPT with Comprehensive Tutorials and Hands-On Projects to Become an Expert in Artificial Intelligence!
What you’ll 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
Description:
Embark on a transformative journey into the world of
Deep Learning
with the course
“Deep Learning A-Z 2024: Neural Networks, AI & ChatGPT Prize.”
This comprehensive course is designed for anyone looking to understand and master the fundamentals of
neural networks
and
artificial intelligence
. From basic concepts to advanced techniques, you’ll learn how to build, train, and deploy deep learning models using the latest tools and frameworks. Explore the cutting-edge applications of
AI
, including the powerful
ChatGPT
, and discover how to create intelligent systems that can transform industries. With hands-on projects and real-world examples, this course will provide you with the skills and knowledge needed to excel in the field of
Deep Learning
.
Link gốc:
https://www.udemy.com/course/deeplearning/
Time Course:
22.5 hours (192 Lectures + Documents)
Instructor
: Kirill Eremenko
Total Weight:
9.82 GB
** Note
:
Chú ý:
Course Content
15 – Mega Case Study
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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!
14 – Building a SOM
-
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
-
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
-
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
-
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
-
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
-
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
-
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
-
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
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