Deep Learning Bootcamp: PyTorch, TensorFlow & Deployment

Deep learning is the engine behind image recognition, language models, and most of modern AI. This bootcamp teaches you how it actually works, then has you build and deploy it yourself.

Most deep learning courses either stay in theory or hand you code to copy. This one does both halves properly. You will understand the math and intuition behind every architecture, then implement it in PyTorch and TensorFlow through a series of hands-on projects, and finally take a trained model from a notebook to a live API on AWS.

What you will build and learn

• Math foundations: linear algebra, calculus, gradients, and probability, taught only as far as you need them for neural networks

• Neural networks from scratch: forward propagation, loss functions, and backpropagation coded by hand before touching a framework

• PyTorch and TensorFlow side by side: tensors, autograd, GradientTape, and building the same networks in both so you can move between them with confidence

• Training done right: activation functions, optimizers, weight initialization, vanishing gradients, overfitting, and evaluation metrics

• Convolutional Neural Networks: image classification, medical imaging, and transfer learning with pretrained models

• RNNs, LSTMs, and Transformers: sequence modeling, tokenization, embeddings, attention, and fine-tuning BERT for text classification

• Model deployment: turning notebooks into scripts, building an inference API with FastAPI, a Streamlit frontend, Git and GitHub, and deploying to an AWS EC2 instance

• Capstone project: an end-to-end image classification app with training pipeline, backend, UI, and cloud deployment

How the course is structured

Concepts are taught on a whiteboard first, so you see the idea before the code. Every theory section is followed by a project section where you apply it. Projects are built in both frameworks under matched conditions, so the comparisons are fair and you learn the real differences rather than one instructor's preference.

Code is kept simple and readable. No unnecessary abstractions, no clever tricks, just the patterns you will actually use.

Who this course is for

• Software developers and data analysts moving into deep learning

• Students and graduates who want practical skills alongside the theory

• Machine learning practitioners who know scikit-learn but have not built neural networks

• Anyone who has tried deep learning tutorials and still feels they are missing the fundamentals

What you need

Basic Python and comfort with high school math. Everything else is covered. The first ten sections run in Google Colab, so no GPU or local setup is required until the deployment section.

What this course is not

It does not cover generative AI, LLM applications, or full MLOps pipelines. Those are separate topics, and a follow-up Generative AI course is available once you have this foundation. This course is focused on giving you a solid, honest understanding of deep learning and the ability to ship a working model.

By the end, you will be able to read a deep learning paper or codebase and know what is going on, build your own models in either framework, and deploy them for others to use.