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Sep 12 to Sep 14

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Sep 19 to Sep 21

Sep 26 Mon

Boston (Weekdays)

Sep 26 to Sep 28

Oct 03 Mon

San Francisco (Weekdays)

Oct 03 to Oct 05

Sep 26 Mon

Washington D.C. (Weekdays)

Sep 26 to Sep 26


1875 Connecticut Ave NW, Washington, DC 20009

Sep 28 Wed

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Sep 28 to Sep 28


79 Madison Ave, New York, 10016, NY

Sep 30 Fri

Boston (Weekdays)

Sep 30 to Sep 30


745 Atlantic Ave, Boston, MA 02111

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Oct 03 to Oct 03


156 2nd St, San Francisco, CA 94105

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Ivy Data Science

Ivy Data Science is an Experfy partner that oversees the transformation of candidates from training, through projects, and placement into full-time employment.

Deep Learning Overivew

Instructor: Ivy Data Science

Overview of Deep Learning Concepts and Applications

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 & 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

TF basic functionality

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