/DeepLearningfromScratch

Deep Learning from Scratch Course

Primary LanguageJupyter NotebookMIT LicenseMIT

Deep Learning from Scratch

This course is organized by the Data Science Group @ UB

Deep learning is one of the fastest growing areas of machine learning and a hot topic in both academia and industry. This course will cover the basics of deep learning by using a hands-on approach.

Course Agenda

  • Introduction to Deep Learning and its applications. Using the Jupyter notebook & Docker.
  • Basic Concepts: Score & Loss functions, Optimization (SGD), Linear Regression.
  • Automated differentiation, Backpropagation, Training a Neural Netwotk from Scratch.
  • Tensorflow programming model.
  • Convolutions & CNN models.
  • Recurrent Neural Netwoks.
  • Unsupervised Learning.
  • Advanced Applications: Neural art, colorization, music generation.

    Course Software Installation

    There’s full documentation on installing Docker at docker.com, but in a few words, the steps are:

    • Go to docs.docker.com in your browser.
    • Step one of the instructions sends you to download the Docker Toolbox.
    • On the Toolbox page, click on the Mac/Windows download button.
    • Run that downloaded file to install the Toolbox.
    • At the end of the install process, chose the Docker Quickstart Terminal.
    • This opens up a new terminal window that runs through an installation script.
    • At the end of the script, you will see ASCII art of a whale and your are left at a prompt.
    • Run this command in the terminal: docker run hello-world
    • This will give you output confirming your installation of docker has worked: Hello from Docker
    • In the docker terminal, run (This operation requires a good internet connection to download ~2.5Gb; it will take some minutes): docker pull deepub/deepub
    • Run the DeepUB image on your system: docker run -p 8888:8888 deepub/deepub
    • Once these steps have been done, you can check the installation by starting your web browser and introducing this URL: http://yourvirtualmachineIP:8888 to access to the fully operational Jupyter notebook of this course. (Note: the address of your virtual machine (yourvirtualmachineIP) is printed at the end of the Docker booting process, just after the ASCII whale).