https://github.com/k-forghani/rayan-ai-imldl

Introduction to Machine Learning and Deep Learning | Rayan AI Contest
https://github.com/k-forghani/rayan-ai-imldl

clustering cnn contest course deep-learning diffusion eda homework knn linear-regression logistic-regression machine-learning neural-network pytorch rayan segmentation sklearn svm vae

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Introduction to Machine Learning and Deep Learning | Rayan AI Contest

• Host: GitHub
• URL: https://github.com/k-forghani/rayan-ai-imldl
• Owner: k-forghani
• Created: 2024-07-24T20:36:15.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-08-23T23:40:02.000Z (about 1 month ago)
• Last Synced: 2024-09-23T06:32:44.161Z (4 days ago)
• Topics: clustering, cnn, contest, course, deep-learning, diffusion, eda, homework, knn, linear-regression, logistic-regression, machine-learning, neural-network, pytorch, rayan, segmentation, sklearn, svm, vae
• Language: Jupyter Notebook
• Homepage:
• Size: 13.9 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Rayan AI: Introduction to Machine Learning and Deep Learning

## Overview

This repository contains the homeworks and assignments for the Introduction to Machine Learning and Deep Learning course at Sharif University of Technology, as part of the Rayan AI Contest.

## Installation

To use the materials, you'll need to set up your development environment. You can do this manually or automatically, depending on your preference.

### Method 1: Manual Setup

This method involves manually installing the necessary tools and packages.

1. **Install Miniconda (or Anaconda)**

   Miniconda is a minimal installer for conda, a package manager that simplifies package management and deployment.

   ```bash

   mkdir -p ~/miniconda3

   wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh

   bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3

   rm -rf ~/miniconda3/miniconda.sh

   ~/miniconda3/bin/conda init bash

   conda config --set auto_activate_base false

   ```

2. **Optimize the Conda Solver**

   To speed up the installation of new packages, I recommend switching to the `libmamba` solver, which is significantly faster than the default solver.

   ```bash

   conda update -n base conda

   conda install -n base conda-libmamba-solver

   conda config --set solver libmamba

   conda config --add channels conda-forge

   ```

3. **Create a New Conda Environment**

   Next, create a new conda environment. Environments help you manage dependencies and prevent conflicts between packages.

   ```bash

   conda create -n ai -y

   ```

4. **Install Necessary Packages**

   With your environment set up, install the required Python packages.

   ```bash

   conda install -c conda-forge -n ai jupyterlab numpy pandas matplotlib seaborn scikit-learn imbalanced-learn opencv -y

   conda activate ai

   pip3 install kaggle

   chmod 600 ~/.kaggle/kaggle.json

   ```

5. **Export the Environment**

   Finally, export the list of installed packages to a YAML file. This allows you to recreate the environment later or share it with others.

   ```bash

   conda activate ai

   conda env export > environment.yml

   ```

### Method 2: Automatic Setup

If you prefer a more automated approach, you can create the environment directly from the provided `environment.yml` file. This method ensures that you install exactly the same packages and versions as specified in the file.

```bash

conda env create -f environment.yml

```