Deep Learning and Neural Networks with PyTorch

Deep learning is the driving force behind modern artificial intelligence, powering everything from computer vision to natural language processing. Understanding how to build these intelligent systems from scratch is an essential skill for anyone looking to enter the field of AI and machine learning. This written course guides you through the core concepts of neural networks and deep learning using PyTorch, one of the industry's most popular frameworks. You will transition from understanding basic mathematical foundations to implementing, training, and optimizing deep neural networks entirely through clear written explanations and structured code snippets. What you'll learn: - Understand the fundamental concepts of neural networks, including tensors, gradients, and backpropagation. - Implement linear, logistic, and softmax regression models using PyTorch's core APIs. - Build and train deep neural networks and convolutional neural networks (CNNs) for image recognition tasks. - Apply modern training techniques, including regularization, optimization algorithms, and learning rate scheduling. - Evaluate model performance using robust validation strategies and modern metrics. - Explore modern PyTorch paradigms, including basic transfer learning and efficient model saving workflows. The course begins with foundational concepts of tensors and gradient tracking before moving step-by-step into building feedforward networks and convolutional architectures. You will progress through structured written explanations, reading and analyzing clean code implementations designed to build your practical intuition. This course is designed for aspiring AI engineers, data scientists, and developers who are new to deep learning and want a solid, code-first introduction to PyTorch without complex prerequisites. Start your journey into deep learning and begin building your own neural networks today.