Let us begin with the basic building blocks of Deep Learning, a Neural Network. A neural network is a computational model inspired by the way human brains process information. Just like our brain has neurons that communicate with each other to make decisions, a neural network consists of artificial neurons, or "nodes," arranged in layers. Each neuron receives inputs, processes them through mathematical functions, and passes the output to the next layer. These networks are widely used in tasks like image recognition, language processing, and decision-making, making them a key tool in modern AI. Refer to these resources carefully to understand what magic goes behind making intelligent systems:
Basic resources:
Advanced Resources (For additional learning purposes only, not necessary):
PyTorch is a powerful, open-source deep learning framework that is widely used for building and training neural networks. It provides an intuitive and flexible platform for creating models, with easy-to-understand dynamic computation graphs, which are especially helpful for research and experimentation. PyTorch is ideal for tasks like natural language processing, computer vision, and reinforcement learning. Its simplicity and strong community support make it a favorite among developers and researchers, especially in academic settings. Some of the resources to begin with PyTorch are mentioned below. You can use either some or all of the resources to learn.