Highlights Cleaned all sections and included new Apptainer guide for HPC compute containerization and LLM introduction and hyperparameter tuning with Gemma 7b (Google) model. 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 Language Models (Libraries: Python, Pytorch, Ollama, LlamaIndex, CUDA, Huggingface, Apptainer) Intro Containers HPC Containers with Apptainer Language Models LLM Introduction & Hyperparameter Tuning 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