Unsupervised Deep Learning in Python

Unsupervised Deep Learning in Python, available at $27.99, has an average rating of 4.6, with 88 lectures, based on 2350 reviews, and has 23809 subscribers.

You will learn about Understand the theory behind principal components analysis (PCA) Know why PCA is useful for dimensionality reduction, visualization, de-correlation, and denoising Derive the PCA algorithm by hand Write the code for PCA Understand the theory behind t-SNE Use t-SNE in code Understand the limitations of PCA and t-SNE Understand the theory behind autoencoders Write an autoencoder in Theano and Tensorflow Understand how stacked autoencoders are used in deep learning Write a stacked denoising autoencoder in Theano and Tensorflow Understand the theory behind restricted Boltzmann machines (RBMs) Understand why RBMs are hard to train Understand the contrastive divergence algorithm to train RBMs Write your own RBM and deep belief network (DBN) in Theano and Tensorflow Visualize and interpret the features learned by autoencoders and RBMs Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion This course is ideal for individuals who are Students and professionals looking to enhance their deep learning repertoire or Students and professionals who want to improve the training capabilities of deep neural networks or Students and professionals who want to learn about the more modern developments in deep learning It is particularly useful for Students and professionals looking to enhance their deep learning repertoire or Students and professionals who want to improve the training capabilities of deep neural networks or Students and professionals who want to learn about the more modern developments in deep learning.

Enroll now: Unsupervised Deep Learning in Python

Summary

Title: Unsupervised Deep Learning in Python

Price: $27.99

Average Rating: 4.6

Number of Lectures: 88

Number of Published Lectures: 79

Number of Curriculum Items: 88

Number of Published Curriculum Objects: 79

Original Price: $27.99

Quality Status: approved

Status: Live

What You Will Learn

Understand the theory behind principal components analysis (PCA)

Know why PCA is useful for dimensionality reduction, visualization, de-correlation, and denoising

Derive the PCA algorithm by hand

Write the code for PCA

Understand the theory behind t-SNE

Use t-SNE in code

Understand the limitations of PCA and t-SNE

Understand the theory behind autoencoders

Write an autoencoder in Theano and Tensorflow

Understand how stacked autoencoders are used in deep learning

Write a stacked denoising autoencoder in Theano and Tensorflow

Understand the theory behind restricted Boltzmann machines (RBMs)

Understand why RBMs are hard to train

Understand the contrastive divergence algorithm to train RBMs

Write your own RBM and deep belief network (DBN) in Theano and Tensorflow

Visualize and interpret the features learned by autoencoders and RBMs

Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion

Who Should Attend

Students and professionals looking to enhance their deep learning repertoire

Students and professionals who want to improve the training capabilities of deep neural networks

Students and professionals who want to learn about the more modern developments in deep learning

Target Audiences

Students and professionals looking to enhance their deep learning repertoire

Students and professionals who want to improve the training capabilities of deep neural networks

Students and professionals who want to learn about the more modern developments in deep learning

Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.

This course is the next logical step in my deep learning, data science, and machine learning series. I’ve done a lot of courses about deep learning, and I just released a course about unsupervised learning, where I talked about clustering and density estimation. So what do you get when you put these 2 together? Unsupervised deep learning!

In these course we’ll start with some very basic stuff – principal components analysis (PCA), and a popular nonlinear dimensionality reduction technique known as t-SNE (t-distributed stochastic neighbor embedding).

Next, we’ll look at a special type of unsupervised neural network called the autoencoder. After describing how an autoencoder works, I’ll show you how you can link a bunch of them together to form a deep stack of autoencoders, that leads to better performance of a supervised deep neural network. Autoencoders are like a non-linear form of PCA.

Last, we’ll look at restricted Boltzmann machines (RBMs). These are yet another popular unsupervised neural network, that you can use in the same way as autoencoders to pretrain your supervised deep neural network. I’ll show you an interesting way of training restricted Boltzmann machines, known as Gibbs sampling, a special case of Markov Chain Monte Carlo, and I’ll demonstrate how even though this method is only a rough approximation, it still ends up reducing other cost functions, such as the one used for autoencoders. This method is also known as Contrastive Divergence or CD-k. As in physical systems, we define a concept called free energy and attempt to minimize this quantity.

Finally, we’ll bring all these concepts together and I’ll show you visually what happens when you use PCA and t-SNE on the features that the autoencoders and RBMs have learned, and we’ll see that even without labels the results suggest that a pattern has been found.

All the materials used in this course are FREE. Since this course is the 4th in the deep learning series, I will assume you already know calculus, linear algebra, and Python coding. You’ll want to install Numpy, Theano, and Tensorflow for this course. These are essential items in your data analytics toolbox.

If you are interested in deep learning and you want to learn about modern deep learning developments beyond just plain backpropagation, including using unsupervised neural networks to interpret what features can be automatically and hierarchically learned in a deep learning system, this course is for you.

This course focuses on “how to build and understand“, not just “how to use”. Anyone can learn to use an API in 15 minutes after reading some documentation. It’s not about “remembering facts”, it’s about “seeing for yourself” via experimentation. It will teach you how to visualize what’s happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

“If you can’t implement it, you don’t understand it”

Or as the great physicist Richard Feynman said: “What I cannot create, I do not understand”.

My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?

After doing the same thing with 10 datasets, you realize you didn’t learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times

Suggested Prerequisites:

calculus

linear algebra

probability

Python coding: if/else, loops, lists, dicts, sets

Numpy coding: matrix and vector operations, loading a CSV file

can write a feedforward neural network in Theano or Tensorflow

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture “Machine Learning and AI Prerequisite Roadmap” (available in the FAQ of any of my courses, including the free Numpy course)

Course Curriculum

Chapter 1: Introduction and Outline

Lecture 1: Introduction and Outline

Lecture 2: How to Succeed in this Course

Lecture 3: Where to get the code and data

Lecture 4: Tensorflow or Theano – Your Choice!

Lecture 5: What are the practical applications of unsupervised deep learning?

Lecture 6: Where does this course fit into your deep learning studies?

Chapter 2: Principal Components Analysis

Lecture 1: What does PCA do?

Lecture 2: How does PCA work?

Lecture 3: Why does PCA work? (PCA derivation)

Lecture 4: PCA only rotates

Lecture 5: MNIST visualization, finding the optimal number of principal components

Lecture 6: PCA implementation

Lecture 7: PCA for NLP

Lecture 8: PCA objective function

Lecture 9: PCA Application: Naive Bayes

Lecture 10: SVD (Singular Value Decomposition)

Lecture 11: Suggestion Box

Chapter 3: t-SNE (t-distributed Stochastic Neighbor Embedding)

Lecture 1: t-SNE Theory

Lecture 2: t-SNE Visualization

Lecture 3: t-SNE on the Donut

Lecture 4: t-SNE on XOR

Lecture 5: t-SNE on MNIST

Chapter 4: Autoencoders

Lecture 1: Autoencoders

Lecture 2: Denoising Autoencoders

Lecture 3: Stacked Autoencoders

Lecture 4: Writing the autoencoder class in code (Theano)

Lecture 5: Testing our Autoencoder (Theano)

Lecture 6: Writing the deep neural network class in code (Theano)

Lecture 7: Autoencoder in Code (Tensorflow)

Lecture 8: Testing greedy layer-wise autoencoder training vs. pure backpropagation

Lecture 9: Cross Entropy vs. KL Divergence

Lecture 10: Deep Autoencoder Visualization Description

Lecture 11: Deep Autoencoder Visualization in Code

Lecture 12: An Autoencoder in 1 Line of Code

Chapter 5: Restricted Boltzmann Machines

Lecture 1: Basic Outline for RBMs

Lecture 2: Introduction to RBMs

Lecture 3: Motivation Behind RBMs

Lecture 4: Intractability

Lecture 5: Neural Network Equations

Lecture 6: Training an RBM (part 1)

Lecture 7: Training an RBM (part 2)

Lecture 8: Training an RBM (part 3) – Free Energy

Lecture 9: RBM Greedy Layer-Wise Pretraining

Lecture 10: RBM in Code (Theano) with Greedy Layer-Wise Training on MNIST

Lecture 11: RBM in Code (Tensorflow)

Chapter 6: The Vanishing Gradient Problem

Lecture 1: The Vanishing Gradient Problem Description

Lecture 2: The Vanishing Gradient Problem Demo in Code

Chapter 7: Applications to NLP (Natural Language Processing)

Lecture 1: Application of PCA and SVD to NLP (Natural Language Processing)

Lecture 2: Latent Semantic Analysis in Code

Lecture 3: Application of t-SNE + K-Means: Finding Clusters of Related Words

Chapter 8: Applications to Recommender Systems

Lecture 1: Recommender Systems Section Introduction

Lecture 2: Why Autoencoders and RBMs work

Lecture 3: Data Preparation and Logistics

Lecture 4: Data Preprocessing Code

Lecture 5: AutoRec

Lecture 6: AutoRec in Code

Lecture 7: Categorical RBM for Recommender System Ratings

Lecture 8: Recommender RBM Code pt 1

Lecture 9: Recommender RBM Code pt 2

Lecture 10: Recommender RBM Code pt 3

Lecture 11: Recommender RBM Code Speedup

Chapter 9: Theano and Tensorflow Basics Review

Lecture 1: (Review) Theano Basics

Lecture 2: (Review) Theano Neural Network in Code

Lecture 3: (Review) Tensorflow Basics

Lecture 4: (Review) Tensorflow Neural Network in Code

Chapter 10: Setting Up Your Environment (FAQ by Student Request)

Lecture 1: Pre-Installation Check

Lecture 2: Anaconda Environment Setup

Lecture 3: How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow

Chapter 11: Extra Help With Python Coding for Beginners (FAQ by Student Request)

Lecture 1: How to Code by Yourself (part 1)

Lecture 2: How to Code by Yourself (part 2)

Lecture 3: Proof that using Jupyter Notebook is the same as not using it

Lecture 4: Python 2 vs Python 3

Lecture 5: Is Theano Dead?

Chapter 12: Effective Learning Strategies for Machine Learning (FAQ by Student Request)

Lecture 1: How to Succeed in this Course (Long Version)

Lecture 2: Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?

Lecture 3: Machine Learning and AI Prerequisite Roadmap (pt 1)

Lecture 4: Machine Learning and AI Prerequisite Roadmap (pt 2)

Chapter 13: Appendix / FAQ Finale

Lecture 1: What is the Appendix?

Lecture 2: BONUS

Instructors

Lazy Programmer Team
Artificial Intelligence and Machine Learning Engineer

Lazy Programmer Inc.
Artificial intelligence and machine learning engineer

Rating Distribution

1 stars: 26 votes

2 stars: 31 votes

3 stars: 84 votes

4 stars: 789 votes

5 stars: 1420 votes

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