Unsupervised Deep Learning in Python

Autoencoders, Restricted Boltzmann Machines, Deep Neural Networks, t-SNE and PCA

Created by: Lazy Programmer Team
Artificial Intelligence and Machine Learning Engineer

Created by: Lazy Programmer Inc.
Artificial intelligence and machine learning engineer

What Will I 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

Requirements

• Knowledge of calculus and linear algebra
• Python coding skills
• Some experience with Numpy, Theano, and Tensorflow
• Know how gradient descent is used to train machine learning models
• Install Python, Numpy, and Theano
• Some probability and statistics knowledge
• Code a feedforward neural network in Theano or Tensorflow

Target audience

• 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