Deep Learning Fundamentals
An Interactive Introduction to Artificial Neural Networks and TensorFlow
Relatively obscure a few short years ago, Deep Learning is ubiquitous today across data-driven applications as diverse as machine vision, natural language processing, and super-human game-playing.
This Deep Learning primer brings the revolutionary machine-learning approach to life with interactive demos from the leading Deep Learning library, TensorFlow, as well as its high-level API, Keras.
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 empowers 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
- Straightforwardly build TensorFlow Deep Learning models using the Keras API
- 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 understand how Deep Learning works.
- You want to create machine-learning models well-suited to solving a broad range of problems, including complex, non-linear problems with large, high-dimensional data sets.
- Experience analyzing data in Python, such as familiarity with the topics covered in Pandas Data Analysis with Python Fundamentals LiveLessons
- 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.
Materials, downloads, or Supplemental Content needed in advance:
- During class, we’ll work on Jupyter notebooks interactively in the cloud via the Safari JupyterHub platform. A link will be provided at the beginning of class.
- Alternatively, if you’d like to run this Live Training’s Jupyter notebooks on your own local machine or server, cross-platform step-by-step instructions are available here.
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 (5 minutes)
Segment 2: Essential Deep Learning Theory (75 min)
- The Cart Before the Horse: A Shallow Neural Network in Keras
- Learning with Artificial Neurons
- TensorFlow Playground—Visualizing a Deep Net in Action
- Break + Q&A (5 minutes)
Segment 3: Deep Learning with Keras, TensorFlow’s High-Level API (60 min)
- Revisiting our Shallow Neural Network
- Deep Nets in Keras
- What to Study Next, Depending on Your Interests