Deep learning and AI technical resources

Education, learning paths, and other technical resources to get you started on your cognitive journey

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Title Type Primary topic area
Learning path: Getting started with PowerAI Vision
SThis learning path is designed for developers interested in quickly getting up to speed on what PowerAI Vision offers and how to use it. The learning path consists of step-by-step tutorials, deep-dive videos, and complete examples of working code.
Learning path PowerAI Vision
Train XGboost models within Watson Machine Learning Accelerator
See how the blend of Watson Machine Learning Accelerator and AC922 offers training performance optimization to accelerate the execution time of XGBoost training.
Tutorial XGBoost
Get dynamic, elastic, and fine-grained resource allocations and controls for accelerating multiple model trainings simultaneously
Use the Watson Machine Learning Accelerator Elastic Distributed Training feature to distribute model training across multiple GPUs and compute nodes
Tutorial Elastic Distributed Training
Train Keras and MLlib models within a Watson Machine Learning Accelerator custom notebook
Customize a notebook package to include Ananconda, PowerAI, sparkmagic and use that to connect to a Hadoop cluster and execute a Spark MLlib model
Tutorial Keras and MLlib
Snap ML: 2x to 40x Faster Machine Learning than Scikit-Learn
Explore this end-to-end deep learning platform for data scientists.
Blog Snap ML
Accelerate machine model training with Watson Machine Learning Accelerator and Snap ML
Explore this end-to-end deep learning platform for data scientists.
Tutorial Snap ML
Snap ML: Examples of use cases from the financial services sector
In this blog, the authors tested generalized linear models (GLMs) from the Snap ML library on three different use cases that are related to the financial services sector.
Blog Snap ML
PowerAI Vision Knowledge Center
Information about planning, installing, configuring, and managing PowerAI Vision.
Knowledge Center PowerAI Vision
Snap ML speed on PowerAI
This series gives you an overview and information on how to use Snap ML, an efficient, scalable machine-learning library that enables fast training of generalized linear models.
Series Snap ML
Watson Machine Learning Community Edition Knowledge Center
Information about planning, installing, configuring, and managing WML CE.
Knowledge Center WML CE
Watson Machine Learning Accelerator Knowledge Center
Information about planning, installing, configuring, and managing WML Accelerator.
Knowledge Center WML Acclerator
Weekly NVIDIA AI Podcast
Get your weekly dose of the latest in AI through this podcast that you can listen to on the go.
Podcast Various
Containerize PowerAI with nvidia-docker
Learn how to build and run Dockerized deep learning analytics using PowerAI libraries on an IBM Power System S822 for High Performance Computing (“Minsky”) system with GPUs.
Tutorial Docker containers
Install NVIDIA CUDA and cuDNN on Power systems
This tutorial explains how to verify whether the NVIDIA toolkit has been installed previously in an environment. It also provides instructions on how to install NVIDIA CUDA on a POWER architecture server.
Tutorial CUDA and cuDNN
Install TensorFlow on Power systems
This tutorial will demonstrate installation of TensorFlow master code on a Power8 server with Ubuntu 16.04, Python 3.5 and NVIDIA CUDA support.
Tutorial TensorFlow
ParallelForAll: Deep Learning
All posts about Deep Learning. See the code or software at work for many Deep Learning challenges.
Blog Various
Bringing the Power of Deep Learning to More Data Scientists
Unlock new analytical insights with PowerAI enterprise software distribution and the Data Science Experience.
Blog Introductory
Tracking the Millennium Falcon with Tensorflow
Learn about using PowerAI + Watson to track the Millenium Falcon
Blog TensorFlow
Machine Learning/Deep Learning performance on IBM Power Systems
Review the machine learning / deep learning performance claims and proof points.
Proof points Performance
Snap ML: A Hierarchical Framework for Machine Learning
In this paper, the authors describe a new software framework for fast training of generalized linear models. The framework, named Snap Machine Learning (Snap ML), combines recent advances in machine learning systems and algorithms in a nested manner to reflect the hierarchical architecture of modern computing systems.
Research paper Snap ML
Efficient Use of Limited-Memory Accelerators for Linear Learning on Heterogeneous Systems
In this paper, the authors propose a generic algorithmic building block to accelerate training of machine learning models on heterogeneous compute systems.
Research paper Acceleration with GPUs
Large-Scale Stochastic Learning using GPUs
In this paper, members of the IBM Research team in Zurich propose an accelerated stochastic learning system for very large-scale applications. Acceleration is achieved by mapping the training algorithm onto massively parallel processors.
Research paper Acceleration with GPUs
IBM PowerAI: Deep Learning Unleashed on IBM Power Systems Servers
This book provides an introduction to AI and deep learning, IBM PowerAI, and components of IBM PowerAI.
IBM Redbook Introductory
TensorFlow Large Model Support Case Study
This blog describes how combining TFLMS with AC922 servers and their NVLink 2.0 connected GPUs allows data scientists to quickly iterate while training with large models and data.
Blog TensorFlow Large Model Support
TFLMS: Large Model Support in TensorFlow by Graph Rewriting
While accelerators such as GPUs have limited memory, deep neural networks are becoming larger and will not fit with the memory limitation of accelerators for training. In this research paper, the authors propose an approach to tackle this problem.
Research paper TensorFlow Large Model Support
TensorFlow Large Model Support Code
This PR proposes a new module, namedlms, in contrib, which helps TensorFlow with training large models that cannot be fit into GPU memory.
GitHub Pull Request TensorFlow Large Model Support
This research paper, published on the Cornell University Library, presents a software-hardware co-optimized distributed Deep Learning system that can achieve near-linear scaling up to hundreds of GPUs.
Research paper Distributed Deep Learning
Classify images with Watson Machine Learning Accelerator
In this tutorial, you will be performing a basic computer vision image classification example using the Deep Learning Impact function within Watson Machine Learning Accelerator (formerly PowerAI Enterprise).
Tutorial Image classification
NVIDIA Deep Learning Institute (DLI)
DLI offers hands-on training for developers, data scientists, and researchers looking to solve challenging problems with deep learning. Learn techniques for designing, training, and deploying neural networks for your domain. Explore common open source frameworks and NVIDIA’s latest GPU-accelerated deep learning platforms.
Online course Acceleration with GPUs
Intro to TensorFlow for Deep Learning
Worldwide experts at Google and the Google Brain project deliver this well-structured long-term course. With a particular emphasis on TensorFlow, you’ll learn skills and complete a multitude of assignments that are similar to real-world problems.
Online course TensorFlow
Using GPUs to Scale and Speed-up Deep Learning
In this course, by, you will learn how to use accelerated GPU hardware to overcome the scalability problem in deep learning.
Online course Acceleration with GPUs
Deep Learning Fundamentals
This course by is great introduction to deep learning concepts, the different kinds of neural networks, and a non-exhaustive catalog of some of the critical frameworks.
See also: Deep Learning with TensorFlow and Accelerating Deep Learning with GPU
Online course Introductory
Deep Learning with TensorFlow
In this course by, begin to practice Deep Learning by learning how to operate with TensorFlow, a key framework included in PowerAI from our collaborators at Google.
See also: Deep Learning Fundamentals and Accelerating Deep Learning with GPU
Online course TensorFlow
Accelerating Deep Learning with GPU
This course by allows you to discover for yourself the value of the POWER architecture and GPU for Deep Learning workloads. Through hands on exercises, the advantage of POWER + GPU becomes obvious.
See also: Deep Learning Fundamentals and Deep Learning with TensorFlow
Online course Acceleration with GPUs