This PyTorch tutorial series is designed for beginners who want to build a strong foundation in deep learning and machine learning using Python. The course provides a structured learning path that starts with installation and gradually builds up to essential deep learning concepts and practical model development.
Learners begin by installing PyTorch and understanding tensor basics, which form the core building blocks of all neural network computations. The course then introduces automatic differentiation (Autograd), explaining how gradients are calculated and used to optimize neural networks. Students also explore backpropagation theory and how it works in real training scenarios.
The course continues with gradient descent and the complete training pipeline, including model definition, loss functions, and optimizers. Learners then move into practical implementations of linear regression and logistic regression, helping them understand both regression and classification tasks in machine learning.
Additional topics include dataset handling, DataLoader usage for batch training, data transformations, and preprocessing techniques. The course also covers essential functions like Softmax and Cross Entropy, as well as activation functions used in neural networks.
By the end of this series, learners will have a solid understanding of PyTorch fundamentals and the complete workflow of training deep learning models. This course is ideal for beginners, Python programmers, and anyone interested in mastering artificial intelligence and neural network development.
