Code Examples for Machine Learning Course at Ural Federal University

Links to the notebooks on Google Colab Platform

1. Introduction to Machine Learning.
2. Training Linear Regression.
3. Polynomial_regression.
4. Classification.
5. MNIST Digits Recognition.
6. MNIST Digits Recognition using neural network.
7. How to detect and prevent overfitting in neural network.
8. An example of code for Kaggle Digit Recognizer competition.
9. Fashion MNIST image recognition.
10. Image Recognition Using Convolutional Neural Network.
11. Image Classification Using Pretrained Neural Networks.
12. Transfer Learning for Cats and Dogs Classification.
13. Cats and Dogs Classification using the InceptionV3 Network.
14. Cats and Dogs Classification using Data Augmentation.
15. Object Detection in Images.
16. Image Segmentation.

Competitions

1. CIFAR-10 Image Classification (Baseline notebook).

Recommended Prerequisite Courses

1. Kaggle Python Course.
2. Kaggle Pandas Course.
3. Kaggle Data Visualization Course.

Recommended books

1. Pro Git
2. Aurélien Géron. Hands-On Machine Learning with Scikit-Learn and TensorFlow. Github repos with code examples from the book and with examples from upcoming second edition.
3. François Chollet. Deep Learning with Python. Github repos with code examples from the book.

Useful links

1. Image Convolution Visualization.
2. A visual and intuitive understanding of deep learning (video).
3. Pretrained neural networks in Keras.
4. Pretrained neural networks in TensorFlow.
5. How to train your own Object Detector with TensorFlow’s Object Detector API.
6. Tensorflow Object Detection API.
7. Understanding Semantic Segmentation with UNET.
8. Up-sampling with Transposed Convolution.

Datasets

1. MNIST.
2. Fashion MNIST.
3. CIFAR-10.
4. ImageNet Dataset.
5. Cats and Dogs.
6. Carvana Image Masking Challenge.