Studyportals

Creative Applications of Deep Learning with TensorFlow

Kadenze

Certificate Online
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Duration
500 USD/full
500 USD/full
Unknown
Tuition fee
Unknown
Unknown
Apply date
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Start date

About

The Creative Applications of Deep Learning with TensorFlow program offered on the Kadenze platform is for anyone curious about how Deep Learning, AI, or Machine Learning can engage with their own ideas or practice—whether you are a traditional computer scientist, psychologist, journalist, creative coder or just curious, no machine learning background is assumed.

Overview

We cover basic fundamentals all the way through state-of-the-art Deep Learning applications. Everything is built using Python and Tensorflow, and applied through the guided homework assignments. Unlike other courses which focus solely on theory and have very little practical guidance for understanding Deep Learning, this course is entirely application-led and taught inside the Python console with real-world examples and code.

The background you learn in the Creative Applications of Deep Learning with TensorFlow program offered on the Kadenze platform will allow you to apply what you learn to other frameworks such as Keras, Caffe, or Theano with greater ease while having a strong foundation in the core components of Deep Learning. We take an approach to learning that requires you to problem solve, applying your work to a creative problem, and interacting with the results of your work with your peers. We also build everything from scratch in TensorFlow and cover techniques for regression, classification, image preprocessing, audio signal processing, audio classification, image synthesis w/ generative networks, recurrent neural network modeling of text, midi, and audio, handwriting modeling and synthesis, and how to train and deploy models in the cloud on Linux systems.

Learning Outcomes

  • TensorFlow Modeling: Create, train, and deploy TensorFlow models
  • Generative Modeling: Apply generative models of image, audio, handwriting, and text using various techniques, such as dilated convolution, mixture density networks, generative adversarial networks, recurrent neural networks, or attention-based recurrent neural networks.
  • Representation Learning: Learn, inspect, and creatively apply representations from deep layers of a pre-trained model to applications such as Deep Dream, Style Net, or Neural Doodle.

Programme Structure

Courses include:

  • Tensorflow
  • Training A Network W/ Tensorflow
  • Cloud Computing, Deploying, TensorBoard
  • Mixture Density Networks
  • Modeling Music and Art: Google Brain’s Magenta Lab
  • Modeling Language: Natural Language Processing

Key information

Start dates & application deadlines

You can apply for and start this programme anytime.

Language

English

Delivered

Online

Academic requirements

We are not aware of any academic requirements for this programme.

English requirements

We are not aware of any English requirements for this programme.

Other requirements

General requirements

  • Some programming experience with Python or similar, e.g. MATLAB, Octave, C/C++, Java, or Processing. 
  • OSX or Linux environments preferred, but Windows users are still supported via "Virtual Machine" and "Docker" which emulates a Linux OS. 
  • Some background with Terminal/Command Line operations. Python 3+ environment (Python 2 users can easily install a new environment for Python 3).

Tuition Fee

  • International

    500 USD/full
    Tuition Fee
    Based on the original amount of 500 USD for the full programme and a duration of  .
  • National

    500 USD/full
    Tuition Fee
    Based on the original amount of 500 USD for the full programme and a duration of  .

Funding

Studyportals Tip: Students can search online for independent or external scholarships that can help fund their studies. Check the scholarships to see whether you are eligible to apply. Many scholarships are either merit-based or needs-based.

Fresh content

Updated in the last 6 months

Check the official programme website for potential updates.

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Creative Applications of Deep Learning with TensorFlow
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