Dr. Jon Krohn
Chief Data Scientist, Author of Deep Learning Illustrated | Untapt
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
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.
Segment 1: The Unreasonable Effectiveness of Deep Learning (40 min)
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
Cost Functions, including Cross-Entropy
Stochastic Gradient Descent
Fancy Optimizers (e.g., Adam, Nadam)
Glorot/He Weight Initialization
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
Software developers, data scientists, analysts, statisticians and other data-related professionals are the core target audience for this training. Moreover, this training is for anyone:
Who would like to be exposed to the range of applications of deep learning approaches.
Who yearn to understand how deep learning works.
Who would like to create state-of-the-art machine-learning models well-suited to solving a broad range of problems, including complex, non-linear problems with large, high-dimensional data sets.
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.
Access to live training and QA session with the Instructor
Certification of completion
“"Jon Krohn is great! His knowledge is so 'deep' and he covers basics and then quickly gets into sophisticated concepts; and, he has an awesome attitude and energy. The material that he has prepared is totally fantastic. His structural framework for understanding; the way he builds on the concepts; and the notebooks to experiment and get started confidently; is a fast and reliable way to get a handle on these technologies and their capabilities" ”
“"Very knowledgeable and experienced instructor. Very good offline materials to follow-up on all of the topics discussed during the training in greater detail."”
“Jon managed to finally break the ice into Deep Learning for me that has been hanging around for quite enough time. Good balance of theory, hands-on modeling and action was the key to grasping the big picture of deep learning overall”