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README

PytorchUltimate

This repo holds material for the Udemy course PyTorch Ultimate: From Basics to Cutting-Edge. You can find the course under this link.

Environment Installation

There are two ways to install the environment: 1. Using venv 2. Using conda

1. venv Environment Installation

Mac, Linux

python3 -m venv venv
source venv/bin/activate
pip install --upgrade pip
pip install -r requirements_py3.txt

Windows

python -m venv venv
.\venv\Scripts\activate
pip install -r requirements_py3.txt

2. Conda Environment Installation

We work with Anaconda and use conda environments. You can replicate my environment by running:

C:\...> conda env create -f pytorch.yml

Environment Installation from scratch

If the installation from yml file fails, you can install the environment manually by running these commands:

C:\...> conda create -n pytorch python=3.10
C:\...> conda activate pytorch
(pytorch) C:\...> conda install pytorch torchvision torchaudio cpuonly -c pytorch
(pytorch) C:\...> conda install ipykernel
(pytorch) C:\...> conda install -c anaconda seaborn
(pytorch) C:\...> conda install scikit-learn
(pytorch) C:\...> $ conda install -c conda-forge detecto

Core symbols most depended-on inside this repo

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Method 152
Class 63
Function 32
Route 3

Languages

Python100%

Modules by API surface

310_PytorchLightning/lightning_intro_end.py10 symbols
200_Autoencoders/Autoencoders_end.py10 symbols
350_SemiSupervised/semi_super_learn_start.py8 symbols
350_SemiSupervised/semi_super_learn_end.py8 symbols
015_NeuralNetworkFromScratch/nn_scratch_end.py8 symbols
360_NLP/90_sentiment_embedding.py7 symbols
360_NLP/25_sentiment_OHE.py7 symbols
190_RecommenderSystems/MatrixFactorization_end.py7 symbols
180_LSTM/FunctionApproximation_incl_extrapolation_end.py7 symbols
180_LSTM/FunctionApproximation_end.py7 symbols
180_LSTM/Flights_end.py7 symbols
045_Classification/MultilabelClassification_end.py7 symbols

For agents

$ claude mcp add PyTorchUltimateMaterial \
  -- python -m otcore.mcp_server <graph>

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