Finally after learning Machine learning very long time ago, I finally got remember that I am an AI enthusiast as now, I am free from front-end. (Although I do not have any repo related to it). So, I decided to create a repository where I will upload the code that I will study, analyze, write or learn while studying deep learning.
This repository serves as a comprehensive technical archive for Deep Learning (DL) research, analysis, and implementation. It tracks the transition from foundational Machine Learning to advanced Neural Network architectures. The core focus of this project is to document the study and reproduction of state-of-the-art models, focusing on efficiency, mathematical intuition, and practical application. Key Technical Areas Covered: Neural Network Fundamentals: Backpropagation, Gradient Descent, and Optimization Algorithms. Computer Vision (CV): Convolutional Neural Networks (CNNs), Object Detection, and Image Segmentation. Natural Language Processing (NLP): RNNs, LSTMs, and Transformer-based architectures. Frameworks: Implementation using PyTorch, TensorFlow, and Keras. Supervised & Unsupervised Learning: Generative Adversarial Networks (GANs) and Autoencoders. Each module includes analyzed code snippets, detailed documentation of model layers, and performance evaluation metrics. This is my plan the rest is on # GOD.