Introduction to Deep Learning with Tensorflow and Keras

Deep Learning (DL) is a fast growing and exciting sub-field of machine learning and AI that has recently made extraordinary advancements in many applications such as image recognition, natural language processing, speech transcription, classification and prediction.

This introductory course focuses on giving you a firm foundation of deep learning techniques using a combination of presentations and hands-on exercises. The presentations introduce essential background and theory and allow for questions and discussions. And the exercises use Jupyter notebooks with Keras and Tensorflow to show you the essentials of building and training neural nets.

Deep learning can transform any industry. This course will help you find creative ways to apply it to yours.

... you squeezed the mystery out of the phrase “deep learning”. And also, that you emphasized that there is some art to this, at least this year, not some one-size-fits-all recipe.

Concrete examples of code and explanation of why to use them and how they work are the best thing. It was great also to have the Q&A.

... the pace of the class. Just right ... I think you hit the spot on this ... your patience with everyone interrupting was impressive! ... answering all questions helped me understand concepts better.

You'll learn:

So that you can:

... to get the answers you need for your business and career.

You'll get the most out of this course if:

Note: You don't need to be a math wizard - this is a hands on practical class. Though knowing more is always helpful, deep understanding of linear algebra and and calculus are not necessary. We will cover all the math basics necessary to get started in the class.

Topics

This course can be given as two separate one day courses or as a back to back two day course.

Day 1

  1. Overview
  2. Fully Connected NNs in Keras
  3. Just Enough Theory
  4. Convolutional NNs
  5. Recurrent Neural Networks

Day 2

  1. Data Prep
  2. Keras Deep Dive
  3. NN Architectures
  4. Transfer Learning
  5. Tensorflow
  6. Production and Deployment

Preparation

This is a hands on course and we'll do the exercises in Jupyter notebooks. You can use a hosted notebook server or you can run jupyter on your own laptop. Any hosted server will do as long TensorFlow and Keras are available or can be installed. We recommend using Google Colabatory.

If you'd like to work with software installed on your own laptop we suggest you download and install Conda and use to to install Python3, Numpy, Pandas, Jupyter, Tensorflow and Keras.

A docker image with the necessary software pre-installed will also be made available.

Also, feel free to bring a CSV of a data set you are interested in exploring and a description of what you'd like to predict.

Schedule

- May 5, 2018 9am - 4pm. Inner SE Portland Oregon   Registration Closed

This is day one from the two day course (1 day total). It will provide a good foundation of deep learning using Keras with at TensorFlow backend.

- March 31, 2018 9am - 4pm. Inner SE Portland Oregon

This is day one from the two day course (1 day total). It will provide a good foundation of deep learning using Keras with at TensorFlow backend.

- Jan 27 and Feb 3, Portland Oregon   Sold Out

This is the two day course spread over two consecutive Saturdays. Space is very limited so I can give everyone enough attention so register soon.


Don't see a date or location that works for you?

Get in touch and lets set something up.

Interested in a shorter overview?

Check out our 2-4 hour Introduction to Deep Learning Workshop


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