Deep Learning with PyTorch

This course offers a comprehensive and practical introduction to deep learning using PyTorch, a leading open-source framework. Learners will develop a solid understanding of foundational concepts such as neural networks, activation functions, forward and backward propagation, and optimization algorithms. Through a structured progression, the course covers essential architectures including perceptrons, multi-layer networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) models, and Transformers. Learners will apply these models to real-world tasks in computer vision and natural language processing, gaining experience in training, evaluating, and optimizing deep learning systems. Advanced topics such as transfer learning, regularization, batch normalization, mixed precision training, attention mechanisms, and model pruning are also explored to help learners build models that are both accurate and efficient. By the end of the course, participants will be equipped with the skills and tools necessary to design and implement deep learning solutions in PyTorch for a wide range of practical applications.