📚 Project Index

This repository covers:

• Parallel Computing using modern ISO C++ and HPC concepts
• Deep Learning fundamentals and hands‑on exercises
• Artificial Intelligence foundations
• Convolutional Neural Networks and Computer Vision
• PyTorch and PyTorch Lightning workflows
• Generative AI and GAN implementations
• Edge‑AI and Jetson setup notes

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🎯 Project Goal

• Build and run Deep Learning networks on a High‑Performance Computing (HPC) platform from scratch.
• Requires strong knowledge of C++, Python, and parallel programming.
• This repository acts as a reference notebook—concise, practical, and intended for readers who already understand the underlying theory.

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🛠 Requirements

• A powerful PC and an Edge Computing Platform (e.g., NVIDIA Jetson REStudio J4011)
• Familiarity with C++, Python, and parallel programming

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📦 Project Structure

The project is divided into Modules, Sub‑chapters, and Exercises.

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Module 1: HPC & Parallelism

Focus: ISO C++ parallelism, GPU acceleration, and HPC fundamentals.
(Lessons are intentionally unordered in this module.)

Files Included

• DAXPY.md
• ISO_CPP_Algorithms_HPC_Documentation.md
• Indexing_in_Parallel_Computing.md
• NVIDIA_Grace_Hopper_Coherent_HW.md
• Parallel_Algorithms_CPP.md
• deep_learning_excercise.md
• excercises.cpp
• excercises_1.cpp
• excercises_2.cpp
• excercises_3.cpp
• extra_excercise_1.cpp
• introduction.md
• main.cpp

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Module 2: Deep Learning Introduction

Foundational concepts and early‑stage neural network understanding.

Files Included

• deepLearningIntroduction.md
• neurel_network_architecture.md
• neural_network_basics.md
• neural_network_introduction.md
• Bias_Variance_Tradeoff.ipynb

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Module 3: Convolutional Neural Networks

CNN fundamentals, preprocessing, augmentation, and full implementations.

Files Included

• convolutional_neural_networks.md
• cnn_architecture.md
• cnn_with_python.md
• cnn_cifar10_full.py
• computer_vision_basics.md
• preprocessing_image_dataset.py
• image_augmentation.py
• convolve_and_pool.py

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Module 4: Advanced Neural Network Topics (including AI Foundations)

Training, optimization, MLPs, RNNs, Transformers, classical ML preprocessing, and more.

Files Included

• training_a_neural_network.md
• classify_news_article.py
• multilayer_perceptron.md
• multilayer_perceptron.py
• recurrent_neural_network.md
• transformer_architecture.md
• types_of_neural_network.md
• lossfunction_for_dl.md
• lossfunction_optimization_algo.md
• hyperparameter_tuning.md
• optimizing_deeplearning_mdl.md
• L1_Regularization.md
• L2_regularization.mde
• elastic_dropout_regularization.md
• wine.csv
• wine_preprocessing_example.py

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Module 5: Build Neural Networks with PyTorch

Pure PyTorch → PyTorch Lightning progression with classification and regression demos.

Files Included

• build_nn_with_PyTorch.md
• pytorch_lightning_demo.py
• pytorch_lightning_classification.py
• pytorch_lightning_regression.py
• run_hparam_tuning.py

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Module 6: Generative AI & GANs

GAN theory, implementation, and experiments with anime faces, Fashion‑MNIST, and text.

Files Included

• GANs.md
• GANs_in_detail.md
• GAN_generator.md
• GAN_dcgan_anime.py
• GAN_anime_real_vs_bad_fake.py
• GAN_anime_real_vs_good_fake.py
• gan_customer_reviews.py
• train_gan_fashion_mnist.py
• generative_ai.md

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Module 7: Edge AI & Jetson

Setup and deployment notes for edge‑AI platforms.

Files Included

• setting_up_jetson.md

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👤 Current Status

All project activities are currently done individually with GPT‑4 assistance.
Once the base version stabilizes, collaborators will be invited to expand the work.

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▶️ Python Example

pip install torch pandas scikit-learn
python wine_preprocessing_example.py
python classify_news_article.py