AI & Deep Learning
The power of algorithms!
Already have programming experience? Here you will delve deep into how neural networks work and learn about various areas of application. Relevant accompanying topics such as data ethics and the strategic use of machine learning algorithms are also covered.
Prior Requirements -
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Must have python knowledge.
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Understanding of data science concepts, including data analysis and statistics.
How to join the track
You can choose this track as part of the local Digital Shaper Program or the remote Code At Home Bootcamp.
While you need to attend in-person for our Digital Shaper Program, the Code at Home Bootcamp provides a convenient option for those unable to access physical locations or seeking a quicker completion pace.
Time
6 months, 5 hours per week or 3 months, 8 hours per week
Certificate
Receive a graduation certificate
by presenting your project
Participants
Over 900 graduates
About
Learn machine learning, including supervised, unsupervised, and reinforcement learning
Understand neural networks, deep learning, and backpropagation
Explore convolutional neural networks, object detection, and natural language processing.
Gain practical experience with Python, Jupyter, and Google Colab
Study data ethics, collaborative filtering, & time series analysis
Master AI deployment, strategies, and working with model hubs
Outcome
Receive a graduation certificate by presenting your project
Access to the course material for free
Support from expert mentors
What is Artificial Intelligence?
See what hides behind the buzzword “AI”!
AI is an expandable definition, which involves learning structures that are able to detect patterns and apply the learned patterns to predict or transform something. When we talk about AI, we talk about deep neural networks or reinforcement learning systems that are capable of solving large, complex problems like object detection, object classification, or autonomous driving. These applications are ruled by deep neural networks with millions of parameters.
Why Artificial Intelligence?
Learn more about the various applications of Artificial Intelligence!
Data is the new oil, but „AI ist the new electricity“ - Andrew Ng. Artificial intelligence helps to mine valuable knowledge from data. Deep neural networks got a boost in popularity in 2011 when the neural network AlexNet solved the ImageNet competition (detection of 1000 classes of objects in images) with an error rate of 16%. Before deep neural networks ruled this competition the average error rate was way above 25%. Since then the error rate has been decreasing to less than 5%. The success of deep neural networks relies on the huge amount of data that is necessary to train the millions of parameters. Since the amount of data is continuously increasing, the range of applications for AI becomes wider and deeper. While AI has been a long time an instrument to solve a single very specific task, AI is more and more developing into a generalized approach for problem solving.
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What will you learn?
Learn more about the insights of our AI & Deep Learning-Track!
Acquire knowledge about deep learning algorithms
Learn how to build image recognition system
Get to know the mathematical foundation behind the training of neural networks
Acquire general python programming knowledge with emphasis on deep learning libraries
