ML at University Ramon Llull-2020
Agenda:
- Overview
- Introduction into ML, why it works
- Pizzeria example, feature and target space
- Linear Regression
- Numpy, Matplotlib, Pandas, Seaborn
- Weather data sample
- Kaggle
- House prices dataset (https://www.kaggle.com/c/house-prices-advanced-regression-techniques)
- EDA: exploratory data analysis (https://www.kaggle.com/pmarcelino/comprehensive-data-exploration-with-python)
- Linear regression example by hand (https://www.kaggle.com/tentotheminus9/linear-regression-from-scratch-gradient-descent)
- Linear regression example by SciKit-Learn
- Deep Learning Book (http://www.deeplearningbook.org)
- Recurrent neural network (https://towardsdatascience.com/recurrent-neural-networks-and-lstm-4b601dd822a5)
- ResNet https://medium.com/@14prakash/understanding-and-implementing-architectures-of-resnet-and-resnext-for-state-of-the-art-image-cf51669e1624
- Generative nets (https://towardsdatascience.com/understanding-generative-adversarial-networks-gans-cd6e4651a29)
- StyleTransfer https://pytorch.org/tutorials/advanced/neural_style_tutorial.html, https://medium.com/@purnasaigudikandula/artistic-neural-style-transfer-with-pytorch-1543e08cc38f
- Reinforcement Learning (https://towardsdatascience.com/introduction-to-various-reinforcement-learning-algorithms-i-q-learning-sarsa-dqn-ddpg-72a5e0cb6287)
- Geometrical Deep Learning (http://geometricdeeplearning.com)
- https://www.deeplearning.ai