Schedule: Deep Learning tools sessions

The TensorFlow library provides computational graphs with automatic parallelization across resources—ideal architecture for implementing neural networks. Michael Cullan walks you through TensorFlow's capabilities in Python, from building machine learning algorithms piece by piece to using the Keras API provided by TensorFlow with several hands-on applications. Read more.

PyTorch is a machine learning library for Python that allows you 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. Join Rich Ott to get the knowledge you need to build deep learning models using real-world datasets and PyTorch. Read more.

Putting together an ML production pipeline for training, deploying, and maintaining ML and deep learning applications is much more than just training a model. Robert Crowe and Pedram Pejman explore Google's TFX, an open source version of the tools and libraries that Google uses internally, made using its years of experience in developing production ML pipelines. Read more.

Federated learning is the approach of training ML models across many devices without collecting the data in a central location. Alex Ingerman explores learning concepts and the use cases for decentralized machine learning, drawing on Google's real-world deployments. You'll learn how to build your first federated models with the open source TensorFlow Federated. Read more.

Container and cloud native technologies around Kubernetes have become the de facto standard in modern ML and AI application development. Antje Barth examines common architecture blueprints and popular technologies used to integrate AI into existing infrastructures and explains how you can build a production-ready containerized platform for deep learning. Read more.

Today, organizations understand the need to keep pace with new technologies when it comes to performing data science with machine learning and deep learning, but these new technologies come with their own challenges. Thomas Phelan demonstrates the deployment of TensorFlow, Horovod, and Spark using the NVIDIA CUDA stack on Docker containers in a secure multitenant environment. Read more.

Developing theoretically principled tools to guide the use of production-scale neural networks is an important practical challenge. Michael Mahoney explores recent work from scientific computing and statistical mechanics to develop such tools, covering basic ideas and their use for analyzing production-scale neural networks in computer vision, natural language processing, and related tasks. Read more.

Over the last few years, convolutional neural networks (CNNs) have risen in popularity, especially in the area of computer vision. Many mobile applications running on smartphones and wearable devices would benefit from the new opportunities enabled by deep learning techniques. Siddha Ganju and Meher Kasam walk you through optimizing deep neural nets to run efficiently on mobile devices. Read more.

Any business, big or small, depends on analytics, whether the goal is revenue generation, churn reduction, or sales or marketing purposes. No matter the algorithm and the techniques used, the result depends on the accuracy and consistency of the data being processed. Sridhar Alla examines some techniques used to evaluate the quality of data and the means to detect the anomalies in the data. Read more.

Laurence Moroney explores how to go from wondering what machine learning (ML) is to building a convolutional neural network to recognize and categorize images. With this, you'll gain the foundation to understand how to use ML and AI in apps all the way from the enterprise cloud down to tiny microcontrollers using the same code. Read more.

The Radeon open ecosystem (ROCm) is an open source software foundation for GPU computing on Linux. ROCm supports TensorFlow and PyTorch using MIOpen, a library of highly optimized GPU routines for deep learning. Jim Dowling and Ajit Mathews outline how the open source Hopsworks framework enables the construction of horizontally scalable end-to-end machine learning pipelines on ROCm-enabled GPUs. Read more.