Deep Learning Bootcamp PyTorch, TensorFlow & DeploymentPublished 9/2026
Created by Siddhardhan S
MP4 |
Video: h264, 1920x1080 |
Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels |
Genre: eLearning |
Language: English |
Duration: 105 Lectures ( 47h 49m ) |
Size: 23.1 GB
⚡ 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
DescriptionDeep 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 structuredConcepts 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
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