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Deep Learning with PyTorch

An Interactive Introduction to Contemporary Artificial Intelligence

Jon Krohn

Relatively obscure a few short years ago, Deep Learning is ubiquitous today across data-driven applications as diverse as machine vision, super-human game-playing, and natural language processing (NLP).

This Deep Learning primer brings the revolutionary approach behind contemporary artificial intelligence to life with interactive demos featuring PyTorch, the wildly popular, paradigm-shifting library for machine learning.

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 straightforward Jupyter notebooks, this foundational knowledge will empower you to build powerful state-of-the-art Deep Neural Network models.

What you'll learn-and how you can apply it

  • Understand the essential theory of artificial neural networks
  • Build dynamic Deep Learning models using the PyTorch library
  • Interpret the output of Deep Learning models to troubleshoot and improve results

This training course is for you because...

  • You work with data and want to be exposed to the range of applications of Deep Learning approaches
  • You want to create Deep Learnings models that are well-suited to solving a broad range of problems, including complex, non-linear problems with large, high-dimensional data sets
  • You may already be familiar with building Deep Learning models in another deep learning library (e.g., TensorFlow, Keras) are are interested in discovering what sets PyTorch apart from these other libraries as well as why PyTorch is being adopted so rapidly by the machine learning community

Prerequisites

  • Experience with an object-oriented programming language, e.g., Python (all code demos during the training will be in Python)
  • Some experience with machine learning would make this Live Training easier to follow, but is by no means necessary

Materials, downloads, or Supplemental Content needed in advance:

  • During class, we’ll work on Jupyter notebooks interactively in the cloud via Google Colab. This requires nearly zero setup and instructions will be provided in class. If you’d like to take a sneak peak at the notebooks we’ll be using, check out github.com/jonkrohn/pytorch

Resources:

About your instructor

  • Jon Krohn is Chief Data Scientist at the machine learning company untapt. He is the presenter of a popular series of tutorials on artificial neural networks, including Deep Learning with TensorFlow, and is the author of Deep Learning Illustrated, the acclaimed book released by Pearson in 2019. Jon holds a doctorate in neuroscience from Oxford University and has been publishing on machine learning in leading academic journals since 2010. He teaches his deep learning curriculum at the NYC Data Science Academy as well as Columbia University. Along with researchers at Columbia’s medical center, Dr. Krohn holds a National Institutes of Health grant to automate medical image processing with deep learning.

Schedule

The timeframes are only estimates and may vary according to how the class is progressing

Segment 1: The Unreasonable Effectiveness of Deep Learning (45 min)

  • Training Overview
  • Introduction to Neural Networks and Deep Learning
  • 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 2.0, Keras, PyTorch, MXNet, CNTK, and Caffe
  • Break + Q&A

Segment 2: Essential Deep Learning Theory (75 min)

  • Hands-On Jupyter Notebook Demo An Artificial Neural Network in PyTorch
  • 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
  • PyTorch: What Sets it Apart from other Deep Learning Libraries
  • Break + Q&A

Segment 3: Deep Learning with PyTorch (60 min)

  • Hands-on Jupyter Notebook Demo: Revisiting our Shallow Neural Network
  • Hands-on Jupyter Notebook Demo: Deep Neural Nets in PyTorch
  • Tuning Model Hyperparameters
  • Creating Your Own Deep Learning Project
  • What to Study Next, Depending on Your Interests