HemSök efter kurserMachine Learning & Deep Learning

Machine Learning & Deep Learning

Deep Learning has in recent years revolutionized research in machine learning and led to AI receiving renewed attention.

In this lecture you will learn how to get started and use artificial neural networks and other deep learning techniques. 


1 dag

9900 kr

Target Group

System Developers


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

Innehåll: 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 


Inregistrering: 08.30
Kursstart: 09.00
Kursslut (ca): 17.00