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Gain basic familiarity with TensorFlow

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.


Module 1: Day 1 - Deep Learning and TensorFlow Overview

Lecture 1 9am – 10am Introductions

Getting to know academic/professional backgrounds of candidates. Introducing the lecturer, course outline, and housekeeping.

Lecture 2 10am – 11am Introduction to Deep Learning

High level overview of the current deep learning landscape. What is Deep Learning? Why is it currently a popular machine learning framework? Applications of Deep Learning.

Lecture 3 11:15am – Noon Code walk through

TF basic functionality

Lecture 4 Noon – 1pm Lab

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

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.

Module 2: Day 2 - Introduction to Convolutional Neural Networks

Lecture 9 9am – 10am Introduction to CNNs

Introduction to convolutional neural networks, CNNs in TensorFlow .

Lecture 10 10am – 11am Code walk through on MNIST data set

Identifying and downloading the data set, Training the model, Evaluating the model.

Lecture 11 11:15am – 1pm Lab

Load MNIST data set, Regression, Testing the model.

Lecture 12 1pm – 2pm Lunch

Lecture 13 2pm – 3pm More on CNNs
Lecture 14 3pm – 4pm Code walk through on MNIST data set
Lecture 15 4:15pm – 6pm Lab

Prepare the data, Build the graph, Build a multilayer Convolutional Network, Train and evaluate the model.

Module 3: Day 3 - More on Convolutional Neural Networks

Lecture 16 9am – 10am TensorFlow Serving

Architecture overview, Servers, Sources, Managers, Download and install TensorFlow Serving.

Lecture 17 10am – 11am Code walk through on TensorFlow Serving

Train and Export TensorFlow Model

Lecture 18 11:15am – 1pm Lab

Using MNIST data set, Train and Export TensorFlow Model, Serve the exported model, Test and run the Server.

Lecture 19 1pm – 2pm Lunch
Lecture 20 2pm – 3pm Further CNN

Further applications using CNNs.

Lecture 21 3pm – 4pm Code walk through on CIFAR-10 data set
Lecture 22 4:15pm – 6pm Lab

Identify and install the CIFAR-10 data set, Basic analysis on CIFAR-10 data set.

Module 4: Day 4 - Introduction to Recurrent Neural Networks

Lecture 23 9am – 10am Introduction to RNNs

Introduction to recurrent neural networks, History, Concepts, LSTM, RNNs in TensorFlow

Lecture 24 10am – 11am Code walk through on language data set

Identify and load Penn Tree Bank dataset, Preliminary analysis

Lecture 25 11:15am – 1pm Lab
Lecture 26 1pm – 2pm Lunch
Lecture 27 2pm – 3pm More on RNNs
Lecture 28 3pm – 4pm Code walk through on language data set

Penn Tree Bank dataset, Analysis

Lecture 29 4:15pm – 6pm Lab

Penn Tree Bank dataset, Further analysis

Module 5: Day 5 - More on Recurrent Neural Networks

Lecture 30 9am – 10am RNNs for machine translation
Lecture 31 10am – 11am Code walk through on second language data set

Machine translation data set

Lecture 32 11:15am – 1pm Lab

Download a machine translation data set, Begin translation

Lecture 33 1pm – 2pm Lunch
Lecture 34 2pm – 3pm More on RNNs
Lecture 35 3pm – 4pm Code walk through on second language data set

WMT 15 data set

Lecture 36 4:15pm – 5:30pm Lab

Completion of machine translation exercise

Lecture 37 5:30pm- 6pm Wrap up