In this repository you will find tutorials and projects related to Machine Learning. I try to make the code as clear as possible, and the goal is be to used as a learning resource and a way to lookup problems to solve specific problems. For most I have also done video explanations on YouTube to make it easier to follow the code. If you got any questions add an issue (or comment on YouTube, I read and answer to most), and if you would like to add an algorithm/improve something please do make a PR! This repository is contribution friendly 😃
- Linear Regression - With Gradient Descent ✅
- Linear Regression - With Normal Equation ✅
- Logistic Regression
- Naive Bayes - Gaussian Naive Bayes
- K-nearest neighbors
- K-means clustering
- Support Vector Machine - Using CVXOPT
- Neural Network
- Decision Tree - Decision Tree by Philip Andreadis
If you have any specific video suggestion please make a comment on YouTube :)
- Tensor Basics
- Feedforward Neural Network
- Convolutional Neural Network
- Recurrent Neural Network
- Bidirectional Recurrent Neural Network
- Loading and saving model
- Custom Dataset (Images)
- Custom Dataset (Text)
- Transfer Learning and finetuning
- Transforms & Data Augmentation
- Learning Rate Scheduler
- Initialization of weights
- TensorBoard Example
- Calculate Mean and STD of Images
- Text Generating LSTM
- Image Captioning
- Neural Style Transfer
- Torchtext [1] Torchtext [2] Torchtext [3]
- Seq2Seq - Sequence to Sequence (LSTM)
- Seq2Seq + Attention - Sequence to Sequence with Attention (LSTM)
- Seq2Seq Transformers - Sequence to Sequence with Transformers
- Transformers from scratch - Attention Is All You Need
- LeNet5 - CNN architecture
- VGG - CNN architecture
- Inception v1 - CNN architecture
- ResNet - CNN architecture
If you have any specific video suggestion please make a comment on YouTube :)
- Tutorial 1 - Installation, Video Only
- Tutorial 2 - Tensor Basics
- Tutorial 3 - Neural Network
- Tutorial 4 - Convolutional Neural Network
- Tutorial 5 - Regularization
- Tutorial 6 - RNN, GRU, LSTM
- Tutorial 7 - Functional API
- Tutorial 9 - Custom Layers
- Tutorial 10 - Saving and Loading Models
- Tutorial 11 - Transfer Learning
- Tutorial 12 - TensorFlow Datasets
- Tutorial 13 - Data Augmentation
- Tutorial 14 - Callbacks
- Tutorial 15 - Custom model.fit
- Tutorial 16 - Custom Loops
- Tutorial 17 - TensorBoard
- Tutorial 18 - Custom Dataset Images
- Tutorial 19 - Custom Dataset Text
- Tutorial 20 - Classifying Skin Cancer - Beginner Project Example