Unsupervised Deep Learning in Python

📚 What you'll 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

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💡 This course delves into the workings of AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion.

💻 It serves as a progression in deep learning, data science, and machine learning education, particularly focusing on unsupervised deep learning.

📊 Fundamental techniques covered include principal components analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) for dimensionality reduction.

🧠 Special attention is given to autoencoders, nonlinear counterparts of PCA, and their role in enhancing supervised deep neural networks.

🌀 Restricted Boltzmann machines (RBMs) are explored as another tool for pretraining deep neural networks, employing methods like Gibbs sampling and Contrastive Divergence (CD-k).

📈 The course illustrates the application of these concepts in visually interpreting patterns learned by unsupervised neural networks, using techniques like PCA and t-SNE on learned features.

🛠️ All materials for the course are freely available, assuming familiarity with calculus, linear algebra, and Python programming, and require installation of essential libraries like Numpy, Theano, and Tensorflow.

🎯 Emphasizing understanding over mere usage, the course encourages experimentation and visualization to grasp internal workings of machine learning models, catering to those seeking in-depth knowledge beyond superficial understanding.