Deep Learning with PyTorch
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About this course
Deep Learning with PyTorch is a Python course on Abekus that builds neural network skills in the order PyTorch practitioners use them. Each lesson is a short reading card with narration and diagrams, followed by MCQ practice with an explanation for every answer. The course moves from perceptrons and backpropagation through tensors, autograd, CNNs and transformers to GPU training, debugging and deployment.
Who this deep learning course is for
The course suits engineering students preparing for machine learning and AI roles in India, and developers who want to move from using ML APIs to building and training models. It also suits data scientists who have used scikit-learn and now need neural network fundamentals. A working knowledge of Python and basic linear algebra helps. If classical ML is still new, Machine Learning Foundations is a better place to begin.
How the lessons and MCQ practice work
Lessons unlock in order, so the perceptron, loss and backpropagation topics come before the tensor, autograd and model-building lessons that rely on them. You read a short card, follow the narration and diagrams, then answer multiple-choice questions on that topic. Every answer comes with an explanation, including for the options you did not pick. The course is guided and self-paced, and it covers data pipelines, optimisation, regularisation, sequence models, transfer learning, debugging, evaluation and serving.
MCQ practice vs video courses for PyTorch
Coursera and Udemy courses are built largely around recorded lectures and notebook walkthroughs, which suits learners who want to watch a model being trained end to end. This course is built around active recall: short reading cards and multiple-choice questions that ask you to pick the right loss for a task, explain why gradients accumulate, or spot a data leakage pattern. Kaggle Learn offers hands-on notebooks for applied ML. The formats complement each other, and many learners alternate between watching a training run and testing their understanding with MCQs.
Areas where PyTorch mistakes usually show up:
- Shape and broadcasting errors that only appear at training time
- Gradient accumulation and forgetting to zero gradients between steps
- Train and eval modes that change dropout and batch norm behaviour
- Data leakage between training, validation and test splits
- Saving, loading and exporting models without losing the state you need
Best way to learn deep learning with PyTorch
Re-implement a small model from memory after each topic, then compare it with the course version. Answer the MCQs before reading explanations, and keep a short log of the questions you miss. Retrieval practice works well for deep learning because a loss function or an activation's saturation behaviour is easy to recognise but harder to explain on a whiteboard. For the transformer and generative model topics, the GenAI and LLM Foundations course is a useful follow-on.