Hands-on Deep Learning with PyTorch: From Math to Modern AI

About this course

“This course contains the use of artificial intelligence.” Deep Learning is the engine powering today's AI revolution, from ChatGPT to autonomous vehicles. In this course, you will learn Deep Learning from the ground up using PyTorch, starting with the core mathematical concepts and building up to modern, state-of-the-art architectures.

You will quickly move into hands-on territory, setting up your environment in Google Colab, mastering PyTorch tensors, and building your first artificial neurons. We demystify the math behind the magic, walking through linear algebra, calculus, and gradients, before applying them to forward passes, loss functions, and backpropagation.

The course dives deep into training robust networks, teaching you how to overcome overfitting using Regularization, Dropout, and Batch Normalization. You will implement various optimizers like SGD and Adam, and apply these skills to a real-world customer churn prediction model. From there, you will conquer image data using Convolutional Neural Networks (CNNs) and Transfer Learning, and tackle sequential data with RNNs and LSTMs for time series and natural language processing.

To ensure you are up to date with modern AI, we break down how Transformers and attention mechanisms work, exploring the differences between BERT and GPT models. You will also dive into Generative Deep Learning, building Autoencoders for anomaly detection and conceptually exploring GANs and Stable Diffusion.

Everything culminates in a comprehensive hands-on capstone project where you will tackle a complex NLP problem, evaluating and comparing Dense Neural Networks, sequence models, and a fine-tuned BERT model to see exactly how different architectures stack up in production.

Course Structure

Concept-based lectures breaking down complex math and neural network theory

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