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✅ Progress Tracker

🔹 Machine Learning Algorithms

  • Linear Regression
  • Logistic Regression
  • Decision Tree
  • Random Forest
  • K-Nearest Neighbors (KNN)
  • Naive Bayes
  • Support Vector Machine (SVM)
  • K-Means Clustering

🔹 Activation Functions

  • Sigmoid
  • Tanh
  • ReLU
  • Leaky ReLU
  • Softmax

🔹 Loss Functions

  • Mean Squared Error (MSE)
  • Binary Cross-Entropy
  • Categorical Cross-Entropy
  • Huber Loss
  • KL Divergence

🔹 Optimizers

  • Gradient Descent
  • Stochastic Gradient Descent (SGD)
  • Momentum Optimization
  • AdaGrad
  • RMSProp
  • Adam

🔹 Neural Network Components

  • Forward Propagation
  • Backward Propagation (Gradient Calculation)
  • Weight Initialization (Xavier, He, Random)
  • Learning Rate Schedulers

🔹 Neural Network Architectures

  • Multi-Layer Perceptron (MLP)
  • Convolutional Neural Network (CNN)
  • Recurrent Neural Network (RNN)
  • Long Short-Term Memory (LSTM)
  • Transformers (Basic Encoder-Decoder)

🔹 Regularization & Optimization

  • L1 & L2 Regularization
  • Dropout
  • Batch Normalization

🔹 Deep Learning Extras

  • Autoencoders
  • Variational Autoencoders (VAEs)
  • Generative Adversarial Networks (GANs)
  • Attention Mechanisms

🛠 Installation

Clone this repo and install dependencies:

git clone https://github.com/Suyog-16/ml-from-scratch.git
cd ml-from-scratch
pip install -r requirements.txt

Requirements

  • Python 3.8+
  • NumPy

Learning Focus

  • Mathematical foundations
  • Algorithmic implementation
  • Performance understanding
  • Numpy numerical computing

License

MIT License

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This repository is dedicated to me trying to implement various machine learning and deep learning concepts scratch

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