Guidelines for the projects presentations, suggestions on what to put on
them, baseline and human performances. What about using transfer
learning, end-to-end approaches, divide the problem into smaller
subproblems, or using multi-tasking? Sometimes the problem can be seen
from another perspective.
Walkthrough a small code used for hyperparameter search: grid search, randomized search, and an example of tensorboard to visualize results
This course is an online course via Zoom.
Our courses are open to all and free of charge for students, pupils, trainees, founders, scientific staff, job seekers, refugees, and pensioners. For employees and self-employed persons, a fee of 200 euros is charged when acquiring a certificate of achievement. Employees and self-employed persons can apply for a scholarship via email@example.com.
You have knowledge about programming and linear algebra (working with vector and matrices) and are interested in getting in-depth knowledge on how to implement machine learning algorithms.
If you are interested in machine learning from a practical perspective, check out also our Machine Learning with Tensorflow course.
The Course Content
This course is based on the first three courses of the online Coursera Specialization "Deep Learning" provided by deeplearning.ai (see https://www.coursera.org/specializations/deep-learning). The instructor is Andrew Ng, co-founder of Coursera, founding lead of the Google Brain Team, and professor in computer science at Stanford University.
The course will provide you with an introduction to machine learning and deep learning, teach you the theoretical foundation behind neural network and how to implement them. You will then apply the newly aquired knowledge to implement your own machine learning project in a team.
How It Works
The course will take place every Monday from 4 pm to 5:45 pm. Before the first meeting there will be a semesterkick-off on the 05.11.2020.
During the week you will be expected to work through the assigned online course content, which will take you between 4 to 5 hours each week, open questions considering the course content and possible additional implications will then be discussed in the weekly offline course on Monday. Towards the end of the semester you will then work in a team on your own machine learning project.
All needed software and online course content is free. For the practical assignments in the online course, however, it will be necessary to create a Google account.
In order to receive a certificate of attendance ("Leistungszertifikat") for this course, active participation is expected, and no more than two classes may be missed. The active participation is proven via the final presentation of your project by you and your team, and the delivery of a well documented project source code. The same conditions apply in order to receive ECTS.
In the online sessions it is necessary that you always provide your full name in Zoom so that your presence is registered on the EDU platform. No mere certificate of attendance will be issued for this course.
Further details may be given in the course.
If you want to learn more about your course lead, check him out here: https://www.linkedin.com/in/luca-palmieri-97209b103/