Machine Learning & Deep Learning

System Developers

The course requires knowledge in programming, it is also an advantage with prior knowledge in data science

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