- Linear Regression
- Logistic Regression
- Decision Tree
- Random Forest
- K-Nearest Neighbors (KNN)
- Naive Bayes
- Support Vector Machine (SVM)
- K-Means Clustering
- Sigmoid
- Tanh
- ReLU
- Leaky ReLU
- Softmax
- Mean Squared Error (MSE)
- Binary Cross-Entropy
- Categorical Cross-Entropy
- Huber Loss
- KL Divergence
- Gradient Descent
- Stochastic Gradient Descent (SGD)
- Momentum Optimization
- AdaGrad
- RMSProp
- Adam
- Forward Propagation
- Backward Propagation (Gradient Calculation)
- Weight Initialization (Xavier, He, Random)
- Learning Rate Schedulers
- Multi-Layer Perceptron (MLP)
- Convolutional Neural Network (CNN)
- Recurrent Neural Network (RNN)
- Long Short-Term Memory (LSTM)
- Transformers (Basic Encoder-Decoder)
- L1 & L2 Regularization
- Dropout
- Batch Normalization
- Autoencoders
- Variational Autoencoders (VAEs)
- Generative Adversarial Networks (GANs)
- Attention Mechanisms
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
- Python 3.8+
- NumPy
- Mathematical foundations
- Algorithmic implementation
- Performance understanding
- Numpy numerical computing
MIT License