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Learn the math & intuition behind Neural Networks, CNNs, RNNs & Transformers. Build projects in PyTorch & TensorFlow
What you'll learn

Understand the math behind deep learning: linear algebra, calculus, gradients, and probability

Build a neural network from scratch in Python with forward and backward propagation coded by hand

Build, train, and evaluate neural networks in both PyTorch and TensorFlow

Build CNNs for image classification and apply transfer learning with pretrained models

Apply RNNs, LSTMs, and Transformers to text, including fine-tuning BERT

Serve a trained model through a FastAPI inference API with a Streamlit frontend

Deploy a deep learning model to AWS EC2 and manage the project with Git and GitHub

Complete an end-to-end capstone: training pipeline, API, UI, and cloud deployment
Requirements

Basic Python: variables, functions, loops, and lists

Basic machine learning: what a model, training data, and accuracy are. No deep learning experience needed

A Google account for Colab. No GPU or local setup needed until the deployment section
Description
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 inPyTorch andTensorFlow through a series of hands-on projects, and finally take a trained model from a notebook to a live API onAWS.
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-tuningBERT for text classification

Model deployment: turning notebooks into scripts, building an inference API withFastAPI, aStreamlit frontend, Git and GitHub, and deploying to anAWS 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

Developers and data analysts moving into deep learning who want real understanding, not copied code

Students and graduates who want project-based skills alongside the theory

ML practitioners who know scikit-learn but have not built or deployed neural networks

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

Code:

https://www.udemy.com/course/deep-learning-bootcamp-pytorch-tensorflow-deployment

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Welcome to Free download educational resource and Apps from TUTBB

Deep Learning Bootcamp PyTorch, TensorFlow & Deployment

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