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Introduction to Deep Belief Network (DBN) with Python 2023
Rating: 4.3 out of 5(24 ratings)
88 students

Introduction to Deep Belief Network (DBN) with Python 2023

Deep Belief Network, Bayesian Belief Network, Restricted Boltzmann Machines, Training DBNs.
Created byHoang Quy La
Last updated 1/2023
English

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)

Course content

5 sections • 17 lectures • 2h 13m total length

  • Course Structure1:55
  • IMPORTANT NOTES PLEASE DO NOT SKIP1:00
  • Overview of DBNs4:43
  • Introduction to BBNs Part 18:37
  • Introduction to BBNs Part 24:16
  • Introduction to RBNs7:10
  • Steps to train RBNs5:59

    Learn the two-step process to train an RBM, including sampling and contrastive divergence, to predict hidden values, reconstruct inputs, and reveal how input data relates to key features.

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 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

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