Published 1/2023MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 997.62 MB | Duration: 2h 12m

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)

Requirements

Deep understanding of Artificial Neural Network

Deep understanding of Convolutional Neural Network

Description

Interested in Machine Learning, Deep Learning, and Artificial Intelligence? Then this course is for you!A software eeer 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. 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 challeg yet lucrative sub-field of Data Science.This course is fun and exciting, but at the same , we dive deep into Deep Belief Networks. Throughout the brand new version of the course, we cover tons of tools and technologies, including:Google ColabDeep Belief Network (DBN)Jupiter NotebookArtificial Neural Network.Neuron.Activation Function.Keras.Pandas.Fine Tuning.Matplotlib.Restricted Boltzmann Machines (RBMs)Contrastive Divergence (CD-k) algorithmTraining DBNsBayesian 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 projectWine projectMovies 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.

Overview

Section 1: Introduction

Lecture 1 Course Structure

Lecture 2 Overview of DBNs

Lecture 3 Introduction to BBNs Part 1

Lecture 4 Introduction to BBNs Part 2

Lecture 5 Introduction to RBNs

Lecture 6 Steps to train RBNs

Section 2: RBM Recommender System

Lecture 7 Introduction to RBM recommender system, importing libraries and loading dataset

Lecture 8 Normalizing the data

Lecture 9 Gibb's sampling Implementation

Lecture 10 RBM recommender system final implementation and showing the result

Section 3: Unsupervised Learning with Deep belief Network

Lecture 11 Unsupervised Learning with Deep belief Network Implementation part 1

Lecture 12 Unsupervised Learning with Deep belief Network Part 2

Lecture 13 Unsupervised Learning with Deep belief Network Final Part

Section 4: Supervised Learning with Deep belief Network

Lecture 14 Supervised Learning with Deep Belief Network Implementation Part 1

Lecture 15 Supervised Learning with Deep Belief Network Implementation Part 3

Section 5: Thank you

Lecture 16 Thank you

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

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