Beschreibung
Get hands-on experience in applying machine learning techniques with TensorFlow.
Learning Goals:
- Mastering best practices for TensorFlow, a popular open-source machine learning framework, to train neural networks
- Handling real-world image data and exploring strategies to avoid overfitting, including techniques like augmentation and dropout
- Building a system capable of processing natural language
- Applying Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs), and Long Short-Term Memory (LSTMs) networks to train models using text and time-series data
What you get
The course instruction is hybrid, which means you can participate either online via Zoom or in person in Kiel (see location listed in the dates).
This course is based on the Coursera Specialization for the TensorFlow Developer Professional Certificate provided by deeplearning.ai. The instructors are Andrew Ng and Laurence Moroney. Andrew Ng was founding lead of the Google Brain Team and is a professor in computer science at Stanford University. Laurence Moroney is an AI developer at Google and author of a large variety of books.
The course will provide you with an introduction to machine learning and deep learning, teach you about computer vision using convolutional neural networks, natural language processing, and how to solve time series and forecasting problems. You will apply the newly aquired knowledge to implement your own machine learning project in a team, and at the end of the course you will have a good basis to further proceed into a machine learning career.
All needed software and online course content is free.
This course is part of the opencampus.sh Machine Learning Degree. Participants of the program for the Machine Learning Degree get preferred access to this course. Find more information on the opencampus.sh Machine Learning Degree here.
What you should bring
You already have some programming knowledge and are interested in getting hands-on knowledge in how to train and use machine learning algorithms.
Also, you should bring sufficient time. During the week you will be expected to work through the assigned online course content, for which you should calculate 5-8 hours each week. With the start of your Machine Learning project (about four to five weeks into the course), you will in addition need several hours a week to work on that.
In order to receive a certificate of achievement (Leistungszertifikat/ ECTS) for this course, active participation is expected, no more than two classes may be missed, and you have to conduct a practice project in a team of 2 to 4 persons. At the end of the course the project has to be presented and a well documented project source code has to be submitted. (Certificates of attendance are not issued i n this course.)