Deep Learning with PyTorch
An Interactive Introduction to Contemporary Artificial Intelligence
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 Learning models.
What you'll learn-and how you can apply it
- Understand the language and fundamentals 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
- 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:
- Installing Jupyter notebooks information to be provided.
- If you’d like to brush up on analyzing data in Python, the topics covered in Pandas Data Analysis with Python Fundamentals LiveLessons will be sufficient for this training
- If you’re the kind of person who likes to be extra-prepared or you can’t wait to get started with Deep Learning, you can view the first three lessons of Jon Krohn’s Deep Learning with TensorFlow LiveLessons in advance of the Live Training, or you can consult Chapters 1 and 5-9 of his book Deep Learning Illustrated
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.
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
- The Deep Learning Families and Libraries
- Break + Q&A
Segment 2: Essential Deep Learning Theory (75 min)
- The Cart Before the Horse: A Shallow Neural Network in PyTorch
- Learning with Artificial Neurons
- PyTorch: What Sets it Apart from other Deep Learning Libraries
- Break + Q&A
Segment 3: Deep Learning with PyTorch (60 min)
- Revisiting our Shallow Neural Network
- Deep Nets in PyTorch
- What to Study Next, Depending on Your Interests