2-Day Training Courses

Michael Li and Russ Martin offer a nontechnical overview of AI and data science. You’ll learn common techniques and how to apply them as well as common pitfalls to avoid. Along the way, you’ll pick up the language of AI and develop a framework to be able to effectively engage with technical experts and utilize their input and analysis for your business’s strategic priorities and decision making. Read more.

PyTorch is a machine learning library for Python that allows users to build deep neural networks with great flexibility. Its easy-to-use API and seamless use of GPUs make it a sought-after tool for deep learning. Ana Hocevar introduces the PyTorch workflow and demonstrates how to use it to build deep learning models using real-world datasets. Read more.

The TensorFlow library provides for the use of computational graphs, with automatic parallelization across resources. This architecture is ideal for implementing neural networks. Dylan Bargteil walks you through TensorFlow's capabilities in Python, teaching you how to build machine learning algorithms piece by piece and use the Keras API provided by TensorFlow with several hands-on applications. Read more.

Francesca Lazzeri, Wee Hyong Tok, and Krishna Anumalasetty walk you through the core steps for using Azure Machine Learning services to train your machine learning models both locally and on remote compute resources. Read more.

Delip Rao and Brian McMahan explore natural language processing with deep learning, walking you through neural network architectures and NLP tasks and teaching you how to apply these architectures for those tasks. Read more.

Machine learning (ML) and deep learning (DL) projects are becoming increasingly common at enterprises and startups alike and have been a key innovation engine for Amazon businesses such as Go, Alexa, and Robotics. Wenming Ye and Miro Enev give you a practical introduction to the next step in DL learning, with lecture, demos, and hands-on labs. Read more.