Deep Learning Bootcamp: PyTorch, TensorFlow & Deployment

About this course

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 modelsRNNs, 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</

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