This Repository contains several machine learning implementations that I learned about while working through Stanfords course on Convolutional Neural Network for Visual Recognition.

Concepts covered:

• K Nearest Neighbor
• Linear Regression
• Support Vector Machine
• Hinge Loss
• Neural Networks
• Backpropogation
• Cross Entropy Loss
• Softmax Linear Classifier

See Conv-Nets Repository for examples on below:

• L2 Regularization
• Batch Processing (Batch Gradient Descent)
• Stochastic Gradient Descent
• Batch Normalization
• Dropout
• hyperparameter optimization techniques

Details about this assignment can be found on the course webpage.