Course Description
Deep Learning (or artificial neural networks) allows us to learn from data, rather than using rule-based software. Neural networks are a technique for building a computer program that learns from data. It is based very loosely on how we think the human brain works. First, a collection of software “neurons” are created and connected together, allowing them to send messages to each other. Next, the network is asked to solve a problem, which it attempts to do over and over, each time strengthening the connections that lead to success and diminishing those that lead to failure. TensorFlow has fast become the de facto framework for deep learning, after being open sourced by Google in November 2015. AI, which currently consists almost entirely of deep learning technologies, is predicted to become exponentially more prevalent over the coming years. Hence the importance of TensorFlow as the framework at the heart of this revolution. We at Ivy Data Science feel, therefore, that knowing TensorFlow well will prove to be extremely valuable in the years ahead.
What am I going to get from this course?
Comfortability with the TensorFlow command line and using TF on basic datasets to extract insights.
Prerequisites and Target Audience
What will students need to know or do before starting this course?
Students should be able to download and install software packages and be comfortable working in a Unix/Linux Shell environment.
Who should take this course? Who should not?
You should be comfortable with Python and Statistics.
Curriculum
Module 1: Day 1 - Deep Learning & TensorFlow Introduction
Lecture 1
9am – 9:30am Introductions
Getting to know academic/professional backgrounds of candidates. Introducing the lecturer, course outline, and housekeeping.
Lecture 2
9:30am – 11am Introduction to Deep Learning
History
Overview
Convolutional Neural Networks
Recurrent Neural Networks
Datasets
Frameworks
TensorFlow
Lecture 3
11:15am – Noon Code walk through
Lecture 4
Noon – 1pm Lab I
Download and install TensorFlow, Test the installation
Lecture 5
1pm – 2pm Lunch
Lecture 6
2pm – 3pm Overview of TensorFlow
Data flow graph, Features, Functionality
Lecture 7
3pm – 4pm TF Code walk through
TensorFlow at the command line, TensorBoard, NN Playground
Lecture 8
4:15pm – 6pm Lab II
Run TF from the command line, Run a demo, Train your first TensorFlow neural net model, Building the computation graph, Launching a graph session, Visualize your training using TensorBoard, Evaluating your model
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