Introduction To Deep Belief Network (DBN) With Python 2023

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 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. 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 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


Homepage

https://www.udemy.com/course/introduction-to-deep-belief-network-dbn-with-python-2023/