🧠 AI & Deep Learning Learning Repository
A collection of machine learning and deep learning implementations, experiments, and practice projects. This repository serves as my ongoing study log as I explore various AI models, algorithms, and frameworks.
I aim to build a strong foundation in both classical ML and modern deep learning by implementing models from scratch, training them on real datasets, and comparing their performance. 📚 What’s Inside
This repository is organized into branches, each dedicated to a specific concept or model:
🔹 AdaBoost
Implementation of Adaptive Boosting for binary classification tasks. Includes intuition, weak learners, and visualization of boosting effects.
🔹 DecisionTree
Decision tree model built from scratch with entropy, Gini index, and pruning logic. Includes visualization of decision boundaries and performance comparisons.
🔹 GAN
Generative Adversarial Networks implemented using PyTorch/TensorFlow. Contains generator–discriminator training loops and experiments with image generation.
🔹 GenerativeAI
Exploration of large language models, prompt engineering, and generative AI applications. Includes projects using LLMs, LangChain, and text generation workflows.
🔹 Gradio
Interactive AI demos built using Gradio interfaces. Useful for quickly deploying and testing ML models in a browser.
🔹 Transformer
Transformer-based NLP models including BERT-encoder pipelines, custom decoder architectures, and IMDB sentiment analysis experiments.
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🎯 Purpose of This Repository • To deepen understanding of machine learning algorithms • To practice implementing models rather than relying solely on libraries • To track growth as an AI/ML practitioner • To build a portfolio of reproducible and testable AI experiments • To explore state-of-the-art deep learning models (GANs, Transformers, LLMs)
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🛠️ Tech Stack • Python • TensorFlow / Keras • PyTorch • Scikit-learn • Jupyter Notebook • Gradio • LangChain • Ollama & LLM frameworks
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📈 Future Goals • Add more branches for vision models (CNN, ResNet, Segmentation) • Implement reinforcement learning algorithms • Build LLM fine-tuning pipelines (LoRA, QLoRA) • Expand dataset variety and include benchmarks • Deploy selected models as web demos
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🙌 Author
Jihoon Jeong AI & Computer Vision Major Passionate about building practical AI systems and exploring the frontier of deep learning.
GitHub: https://github.com/jeehun3020