Course Abstract

Training duration : 4 hours

Relatively obscure a few short years ago, deep learning is ubiquitous today across data-driven applications as diverse as machine vision, natural language processing, artistic creativity, and complex sequential decision-making. This deep learning primer brings the revolutionary approach behind contemporary artificial intelligence to life with interactive demos featuring TensorFlow 2 and PyTorch, the two leading deep learning libraries. To facilitate an intuitive understanding of deep learning’s artificial-neural-network foundations, essential theory will be introduced visually and pragmatically. Paired with tips for overcoming common pitfalls and hands-on code run-throughs provided in Python Jupyter notebooks, this foundational knowledge empowers you to build powerful state-of-the-art deep neural network models. Many resources will be provided for digging further into any deep learning-related topic that piques your interest.

What other students say about this session?

“The content was interesting, at just the right level of detail (not too over heads for people new to the topic but not so basic as to be a waste of time), and engaging. I loved the style and flow of the course, with lots of visuals and examples to keep us engaged. This is the best workshop/training/presentation I have attended in a long time. ”

“Jon did an excellent delivery of such a complex topic and gave a thorough, non boring, presentation within such a discreet time. He have a presentation covering from the history of deep learning to actual execution of code to presenting helpful resources. It was a true pleasure to take this course. So much information was given that I need time to process all that wealth of information but feel confident I can with all the resources Jon provided. Thank you ”

“I enjoyed the presentation and examples used to breakdown Deep Learning into digestible parts. ”

Learning Objectives

  • Understand the essential theory of artificial neural networks, including which deep learning approach is most appropriate for solving a given problem

  • Build production-ready deep neural networks with the NumPy-esque PyTorch library as well as with the heavyweight TensorFlow 2 library (by taking advantage of its in-built, easy-to-use Keras module)

  • Interpret the output of deep learning models to troubleshoot and improve results

DIFFICULTY LEVEL: INTERMEDIATE

Instructor

Instructor Bio:

Jon Krohn is Chief Data Scientist at the machine learning company untapt. He authored the 2019 book Deep Learning Illustrated, an instant #1 bestseller that was translated into six languages. Jon is renowned for his compelling lectures, which he offers in-person at Columbia University, New York University, and the NYC Data Science Academy. Jon holds a Ph.D. in neuroscience from Oxford and has been publishing on machine learning in leading academic journals since 2010; his papers have been cited over a thousand times.

Dr. Jon Krohn

Chief Data Scientist, Author of Deep Learning Illustrated | untapt

Course Outline

Segment 1: The Unreasonable Effectiveness of Deep Learning (40 min)


  • Training Overview 

  • A Brief History of the Rise of Deep Learning

  • Deep Learning vs Other Machine Learning Approaches

  • Dense Feedforward Networks

  • Convolutional Networks for Machine Vision

  • Recurrent Networks for Natural Language Processing and Time-Series Predictions

  • Deep Reinforcement Learning for Sequential Decision-Making

  • Generative Adversarial Networks for Creativity

  • Overview of the Leading Deep Learning Libraries, including TensorFlow 2, Keras, PyTorch, MXNet, CNTK, and Caffe


Segment 2: Essential Deep Learning Theory (80 min)


  • An Artificial Neural Network with Keras

  • The Essential Math of Artificial Neurons

  • The Essential Math of Neural Networks

  • Activation Functions

  • Cost Functions, including Cross-Entropy

  • Stochastic Gradient Descent

  • Backpropagation

  • Mini-Batches

  • Learning Rate

  • Fancy Optimizers (e.g., Adam, Nadam)

  • Glorot/He Weight Initialization

  • Dense Layers

  • Softmax Layers

  • Dropout

  • Data Augmentation

  • TensorFlow Playground: Visualizing a Deep Net in Action 


Segment 3: TensorFlow 2 and PyTorch (90 min)


  • Revisiting our Shallow Neural Network

  • Deep Neural Nets in TensorFlow 2

  • Deep Neural Nets in PyTorch

  • Tuning Model Hyperparameters

  • Creating Your Own Deep Learning Project

  • What to Study Next, Depending on Your Interests

Background knowledge

  • Some experience with machine learning would make this workshop easier to follow, but is by no means necessary.

  • All code demos during the training will be in Python, so experience with it or another object-oriented programming language would be helpful.