Cleaned Stable Release

Highlights

Cleaned documentation, code, and website with a particular emphasis on sections and subsections to demarcate tutorials for ease of learning and ensure scalability in their respective sections.

Sections and Subsections

• Deep Learning and Deep Reinforcement Learning Tutorials (Libraries: Python, PyTorch, Gym, NumPy, Matplotlib and more)

  • Introduction
  • Course Progression
  • Practical Deep Learning with PyTorch
    • Matrices
    • Gradients
    • Linear Regression
    • Logistic Regression
    • Feedforward Neural Network (FNN)
    • Convolutional Neural Network (CNN)
    • Recurrent Neural Network (RNN)
    • Long Short-Term Memory Network (LSTM)
    • Autoencoders (AE)
    • Fully Connected Overcomplete Autoencoders
  • Improving Deep Learning with PyTorch
    • Derivative, Gradient and Jacobian
    • Forward- and Backward-propagation and Gradient Descent (From Scratch FNN Regression)
    • Learning Rate Scheduling
    • Optimization Algorithms
    • Weight Initialization and Activation Functions
  • Deep Reinforcement Learning with PyTorch
    • Supervised to Reinforcement Learning
    • Markov Decision Processes and Bellman Equations
    • Dynamic Programming
  • From Scratch Deep Learning with PyTorch/Python
    • From Scratch Logistic Regression Classification
  • Compute Optimization
    • Speed Optimization Basics Numba

• Machine Learning Tutorials (Libraries: Python, cuDF RAPIDS, cuML RAPIDS, pandas, numpy, scikit-learn and more)

  • RAPIDS cuDF
    • Intro
    • GPU DataFrames
    • CPU/GPU Fractional Differencing

• Programming Tutorials (Libraries: C++, Python, Bash and more)

  • Intro
  • C++
  • Bash
  • Python
  • Javascript
  • Electron

• Data Engineering Tutorials (Libraries: Bash, Databricks, Delta Live Tables, Parquet, Python, Cassandra, and more)

  • Cassandra (NoSQL)
    • Introduction
    • Apache Cassandra Cluster Setup