Neural-Networks

This repository is a comprehensive learning resource on Neural Networks and Deep Learning, designed for beginners as well as advanced practitioners in Machine Learning and Artificial Intelligence (AI). It covers both the theory and implementation of neural networks, starting from the basics of perceptrons, activation functions, forward and backpropagation, to advanced architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), LSTMs, Autoencoders, GANs, and Transformers. The theory section explains concepts with detailed notes, while the code section provides practical implementations in Python, using NumPy, TensorFlow, and PyTorch. In addition, the projects showcase real-world applications including image classification, text sentiment analysis, stock price prediction, and generative modeling. This repository is ideal for students, researchers, and developers who want to master neural networks from scratch and apply them in practical AI/ML use cases. A complete reference for both learning and hands-on experimentation in deep learning.