Deep Learning from Scratch

This directory contains Jupyter's notebook-based documentation for the Deep Learning from Scractch course. November 2016.

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.

Approach

We will illustrate all contents with Jupyter notebooks, a web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text.

Target Audience

This course is targeted for developers, data scientists and researchers that have a basic knowledge of machine learning.

Prerequisites

Minimal experience on Python programming, basic knowledge of calculus, linear algebra, and probability theory. Attendees are expected to bring their own laptops for the hands-on practical work.

Who

This course is organized by the Data Science Group @ UB

INSTRUCTORS: Oriol Pujol, Associate Professor at UB. Santi Seguí, Lecturer at UB. Jordi Vitrià. Full Professor at UB.

Why

By the end of this course, you will be able to:

• Describe how a neural network works and combine different types of layers and activation functions.
• Describe how these models can be applied in computer vision, text analytics, etc.
• Develop your own models in Tensorflow.

Topics

• Basic Concepts I
• Basic Concepts II
• Tensorflow
• Convolutional Neural Networks
• Recurrent Neural Networks
• Unsupervised Learning
• Advanced Applications