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Deep Learning Bootcamp PyTorch, TensorFlow & Deployment
Published 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
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 deploymentRequirements
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 sectionDescription
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 networksNeural networks from scratch: forward propagation, loss functions, and backpropagation coded by hand before touching a frameworkPyTorch and TensorFlow side by side: tensors, autograd, GradientTape, and building the same networks in both so you can move between them with confidenceTraining done right: activation functions, optimizers, weight initialization, vanishing gradients, overfitting, and evaluation metricsConvolutional Neural Networks: image classification, medical imaging, and transfer learning with pretrained modelsRNNs, LSTMs, and Transformers: sequence modeling, tokenization, embeddings, attention, and fine-tuningBERT for text classificationModel deployment: turning notebooks into scripts, building an inference API withFastAPI, aStreamlit frontend, Git and GitHub, and deploying to anAWS EC2 instanceCapstone project: an end-to-end image classification app with training pipeline, backend, UI, and cloud deploymentHow 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 fundamentalsHomepage
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