Introduction to TensorFlow
Learn the basics of machine learning, deep learning using TensorFlow
Get started with machine learning using TensorFlow, the popular open source machinelearning software developed by Google’s Brain team.
In this online course, you’ll learn core concepts in machine learning and TensorFlow with a focus on neural networks. You'll gain experience working with TensorFlow’s collection of visualization tools, including building and launching graphs in TensorFlow and using TensorBoard to visualize workflow and evaluate model performance. The instructors use notebooks with interactive code segments to guide you as you apply machinelearning concepts and build algorithms that make predictions using realworld datasets. By the end of the course, you’ll know how and when to use TensorFlow in your own applications.
What you'll learnand how you can apply it
By the end of this live, online course, you’ll understand:
 The basics of machine learning, neural networks, deep learning, and artificial intelligence
 What TensorFlow is and what applications it is good for
And you’ll be able to:
 Create statistical models for classification and regression using TensorFlow
 Evaluate the benefits and disadvantages of using TensorFlow over other machinelearning software
This training course is for you because...
 You’re a software engineer or programmer with a background in Python, and you want to develop a basic understanding of machine learning.
 You have experience modeling or a background in data science, and you’d like to learn TensorFlow.
 You’re in a nontechnical role, and you’d like to more effectively communicate about TensorFlow and neural networks with the engineers and data scientists in your company.
Prerequisites
 A basic understanding of Python and modeling
 Familiarity with matrices and linear algebra
Materials and downloads needed:
 A machine with TensorFlow installed and set up prior to the training
About your instructor

Michael is the CEO and Founder of The Data Incubator. While working as Head Data Scientist at Foursquare, he discovered that his favorite part of the job was teaching and mentoring smart people about data science, which lead to the creation of The Data Incubator. He also has worked as a quant (D.E. Shaw, J.P. Morgan), and a rocket scientist (NASA). He completed his PhD at Princeton as a Hertz fellow and read Part III Maths at Cambridge as a Marshall scholar.
From helping hiring companies find qualified candidates for data driven roles to providing handson corporate trainings, Michael has enjoyed building his startup that lets him focus on what he loves.

Dana Mastropole is a Data Scientist in Residence at the Data Incubator. She studied physics as an undergraduate at Georgetown University and received her master’s degree in physical oceanography from MIT. She also completed MIT’s Kaufman teaching certificate program andtaught elementary school science prior to joining the Data Incubator team.

Robert Schroll is a Data Scientist in Residence at The Data Incubator and has been a key contributor to a variety of open source software development and data science projects. He obtained his Ph.D. in computational physics from the University of Chicago before completing postdocs in Amherst, Massachusetts, and Santiago, Chile. There, he realized that the favorite parts of his job were teaching and analyzing data. He made the switch to data science and hasbeen teaching at the Data Incubator for the past six months.
Schedule
The timeframes are only estimates and may vary according to how the class is progressing
Day 1: Introduction to TensorFlow
What are machine learning and TensorFlow? (20 minutes)
TensorFlow basics: Data types and iterative algorithms (20 minutes)
Building and launching graphs in TensorFlow (20 minutes)
Break (10 minutes)
Implementing Newton’s method of root finding (20 minutes)
Using the TensorBoard (20 minutes)
Q&A (20 minutes)
Assignment: Continue working on inclass exercise
Day 2: Introduction to machine learning
Machine learning basics: Linear regression (20 minutes)
Machine learning basics: Logistic regression (20 minutes)
Building a model to classify flowers in the iris dataset (20 minutes)
Break (10 minutes)
Basic neural networks (20 minutes)
Neural networks in TensorFlow (20 minutes)
Q&A (20 minutes)
Assignment: Continue working on inclass exercise
Day 3: Introduction to deep learning
Deep learning and deep neural networks (20 minutes)
Training a deep neural network to classify handwritten digits (20 minutes)
Basic versus convolutional neural networks: Performance evaluation (20 minutes)
Break (10 minutes)
Convolutional neural networks (20 minutes)
Generative networks (20 minutes)
Q&A (20 minutes)