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

Understand the math behind neural networks, then build CNNs, Transformers step by step in Python

What you’ll learn
Understand the foundations of Deep Learning, from basic math to modern architectures like Transformers and Diffusion models
Master PyTorch basics, including tensor manipulation, GPU acceleration, and creating neural networks from scratch
Grasp the essential math behind AI, including linear algebra, calculus, derivatives, and the chain rule for backpropagation
Build, train, and optimize deep neural networks using techniques like Dropout, Batch Normalization, and Adam optimizers
Develop Convolutional Neural Networks (CNNs) for image classification and apply Transfer Learning with pre-trained models like ResNet
Analyze sequential data using RNNs and LSTMs for sentiment analysis and time series forecasting
Understand and implement the self-attention mechanism and Transformer architectures (BERT vs GPT)
Explore Generative Deep Learning by building Autoencoders for anomaly detection and understanding GANs and Diffusion models
Build an end-to-end Capstone Project comparing Dense Networks, LSTMs, and fine-tuned BERT models

Requirements
No prior deep learning experience needed
Basic understanding of Python programming is required
High-school level mathematics is helpful, though core concepts are reviewed in the course
A Google account to access Google Colab (no local GPU required)

Description
“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

Hands-on PyTorch coding demonstrations using Google Colab

Deep dives into CNNs, LSTMs, Transformers, and Generative DL

Practical implementation of model optimization, regularization, and evaluation

End-to-end Capstone Project comparing multiple deep learning architectures

Course Contents
What is Deep Learning & Why it Matters Now

Math You Need for Deep Learning

How Neural Networks Learn

Training a Network That Actually Works

Convolutional Neural Networks (CNNs)

Sequence Models — RNNs & LSTMs

Transformers & Attention – How GPT Works

Generative Deep Learning – Autoencoders, GANs & Diffusion Models

Capstone Project – From Dense Networks to BERT

Special Note
This course is built around clarity and hands-on learning. Every technical concept is paired with a simple analogy so it sticks, with a live demonstration so you see it working in real time. The end-to-end project at the end of the course are designed to bring together everything you have learned – so you leave with working projects, not just notes.

Who this course is for
Beginners who want a structured, intuitive introduction to Deep Learning and PyTorch
Python developers and data professionals looking to transition into AI and machine learning engineering
Students and professionals who want to understand the actual math and mechanics behind neural networks, not just high-level APIs
Anyone who prefers practical, code-first learning using cloud-based notebooks (Google Colab)

FHNDONDEEPLEARNINGWITHPYTORCHF

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