Machine Learning & Deep Learning

Birger Moëll Machine Learning Research Engineer KTH / Ayond
09:00-09:30 Introduction to Machine learning / Deep Learning  | Talk |
Introduction to Machine Learning Slides
09:30-09:45 Getting your machines ready for machine learning | Code |   
Write code for installing properly on mac and windows
Install for windows and mac, keras, tensorflow, numpy.
09:45-10:00 Coffee and break    
10:00-10:15 Hello World in Machine Learning (MNIST) | Talk |  
Code for MNIST. Explanation of MNIST
10:15-10:45 Running your own MNIST | Code |   
Getting your computer working with MINST
Exploring MNIST and running different models for solving MNIST
10:45-11:00 Coffee and break    
11:00-11.15 Feedforward Neural Networks | Talk |  
What is a neural network, activation functions, math behind, neuroscience
11.15-11.45 Building your own feedforward neural network | Code |  
Build a neural network to handle data from neuroscience (the data is processed, you just need to build the network)
11:45-12:45 Lunch
12:45-13:00 Q and A | Interactive
13:00-13.15 Image recognition and convolutional neural networks | Talk |  
Slides regarding image recognition, how it works, neuroscience, math
13:15-13:45 Building your own convolutional neural network | Code |   
Training a classifier of cat vs dog
13:45-14:00 Coffee and break    
14:00-14.15 Time series prediction and LSTMs | Talk |  
Slides regarding time series data and LSTMs, What are LSTMs useful for.
14:15-14:45 Building your own LSTMs | Code |    
Borrow from the unreasonable effectiveness of LSTMS, jupyter notebook for working with LSTM data.
14:45-15:00 Coffee and break    
15:00-15.15 Generative models | Talk |  
Talking about generative models, how can they be used.
15:15-15:45 Trying out GANS | Code |    
Code for style transfer? Generative models
15:45-16:00 Coffee and break   
16:00-16.15 Machine learning in the wild | Talk |
How to host your models, Flask, Google, AWS, Azure, Tensorflow.js
16:15-16:45 Serving your own machine learning model | Code |  
Building your own flask model to train.
16:45-17:00 Q and A | Interactive