Introduction to Deep Belief Network (DBN) with Python 2023

Focused View

Hoang Quy La

2:12:22

388 View

1 - Introduction

1 - Course Structure.mp4

01:55

2 - Overview of DBNs.mp4

04:43

2 - deep-belief-network-introduction.zip

3 - Introduction to BBNs Part 1.mp4

08:37

3 - deep-belief-network-introduction-1.zip

4 - Introduction to BBNs Part 2.mp4

04:16

4 - deep-belief-network-introduction-1.zip

5 - Introduction to RBNs.mp4

07:10

5 - deep-belief-network-introduction-1.zip

6 - Steps to train RBNs.mp4

05:59

6 - deep-belief-network-introduction-2.zip

2 - RBM Recommender System

7 - Introduction to RBM recommender system importing libraries and loading dataset.mp4

10:32

7 - deep-belief-network-dataset-20230105T040127Z-001.zip

7 - deep-belief-network-introduction-3.zip

8 - Normalizing the data.mp4

09:21

8 - deep-belief-network-introduction-4.zip

9 - Gibbs sampling Implementation.mp4

13:41

9 - deep-belief-network-introduction-5.zip

10 - RBM recommender system final implementation and showing the result.mp4

14:42

10 - deep-belief-network-introduction-6.zip

3 - Unsupervised Learning with Deep belief Network

11 - Unsupervised Learning with Deep belief Network Implementation part 1.mp4

17:39

11 - unsupervised-learning-implementation-with-dbn.zip

12 - Unsupervised Learning with Deep belief Network Part 2.mp4

05:42

13 - Unsupervised Learning with Deep belief Network Final Part.mp4

05:24

13 - unsupervised-learning-implementation-with-dbn-1.zip

4 - Supervised Learning with Deep belief Network

14 - Supervised Learning with Deep Belief Network Implementation Part 1.mp4

14:05

14 - supervised-learning-implementation-with-dbn.zip

15 - Supervised Learning with Deep Belief Network Implementation Part 3.mp4

07:20

5 - Thank you

16 - Thank you.mp4

01:16

Description

Deep Belief Network, Bayesian Belief Network, Restricted Boltzmann Machines, Training DBNs.

What You'll Learn?

Deep Belief Network (DBN)
Restricted Boltzmann Machines (RBMs)
Contrastive Divergence (CD-k) algorithm
Training DBNs
Fine-tuning
Bayesian Belief Networks (BBNs)

Who is this for?

Anyone interested in Deep Learning, Machine Learning and Artificial Intelligence

Students who have at least high school knowledge in math and who want to start learning Machine Learning, Deep Learning, and Artificial Intelligence

Any data analysts who want to level up in Machine Learning, Deep Learning and Artificial Intelligence.

Anyone passionate about Artificial Intelligence

Data Scientists who want to take their AI Skills to the next level

More details

Description
Interested in Machine Learning, Deep Learning, and Artificial Intelligence? Then this course is for you!
A software engineer has designed this course. With the experience and knowledge I gained throughout the years, I can share my knowledge and help you learn complex theories, algorithms, and coding libraries.
I will walk you into Deep Belief Networks.Â There are no courses out there that cover Deep Belief networks. However, Deep Belief Networks are used in many applications such as Image recognition, generation, and clustering, Speech recognition, Video sequences, and Motion capture data. So it is essential to learn and understand Deep Belief Network. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.
This course is fun and exciting, but at the same time, we dive deep into Deep Belief Networks. Throughout the brand new version of the course, we cover tons of tools and technologies, including:
Google Colab
Deep Belief Network (DBN)
Jupiter Notebook
Artificial Neural Network.
Neuron.
Activation Function.
Keras.
Pandas.
Fine Tuning.
Matplotlib.
Restricted Boltzmann Machines (RBMs)
Contrastive Divergence (CD-k) algorithm
Training DBNs
Bayesian Belief Networks (BBNs)
Moreover, the course is packed with practical exercises based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your models. There are three big projects in this course. These projects are listed below:
MNIST project
Wine project
Movies project.
By the end of the course, you will have a deep understanding of Deep Belief Networks, and you will get a higher chance of getting promoted or a job by knowing Deep belief Networks.
Who this course is for:
Anyone interested in Deep Learning, Machine Learning and Artificial Intelligence
Students who have at least high school knowledge in math and who want to start learning Machine Learning, Deep Learning, and Artificial Intelligence
Any data analysts who want to level up in Machine Learning, Deep Learning and Artificial Intelligence.
Anyone passionate about Artificial Intelligence
Data Scientists who want to take their AI Skills to the next level

User Reviews

Rating

average 0

Total votes0

Focused display

My name is Hoang Quy La. I did graduate from RMIT University as a first class honours in electrical engineering and I am currently studying master of software engineering in CDU at Australia. I have taught over 1250 students with 5 star reviews. I did develop a AI Chatbot with Tensorflow 2.0 with Flask by using Python and this Chatbot was implemented in the top University in Viet Nam. My current project is about AI in Healthcare applications. I also did complete my internship at SGS and Power System Company. Check my LinkedIn for all projects which I did in AI field.

Udemy

View courses Udemy

Students take courses primarily to improve job-related skills.Some courses generate credit toward technical certification. Udemy has made a special effort to attract corporate trainers seeking to create coursework for employees of their company.