If you are anything like me, Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning are completely fascinating and exciting topics. As AI, ML, and Deep Learning become more widely used, for me it means that the science fiction written by Dr. Issac Asimov, the robotics and medical advancements in Star Wars, and the technologies that enabled Captain Kirk and his Star Trek crew “to boldly go where no man has gone before” can become achievable realities.
Most people interested in the aforementioned topics are familiar with the AI and ML solutions enabled by Deep Learning, such as Convolutional Neural Networks for Image and Video Classification, Speech Recognition, Natural Language interfaces, and Recommendation Engines. However, it is not always an easy task setting up the infrastructure, environment, and tools to enable data scientists, machine learning practitioners, research scientists, and deep learning hobbyists/advocates to dive into these technologies. Most developers desire to go quickly from getting started with deep learning to training models and developing solutions using deep learning technologies.
For these reasons, I would like to share some resources that will help to quickly build deep learning solutions whether you are an experienced data scientist or a curious developer wanting to get started.
Deep Learning Resources
The Apache MXNet is Amazon’s deep learning framework of choice. With the power of Apache MXNet framework and NVIDIA GPU computing, you can launch your scalable deep learning projects and solutions easily on the AWS Cloud. As you get started on your MxNet deep learning quest, there are a variety of self-service tutorials and datasets available to you:
- Launch an AWS Deep Learning AMI: This guide walks you through the steps to launch the AWS Deep Learning AMI with Ubuntu
- MXNet – Create a computer vision application: This hands-on tutorial uses a pre-built notebook to walk you through using neural networks to build a computer vision application to identify handwritten digits
- AWS Machine Learning Datasets: AWS hosts datasets for Machine Learning on the AWS Marketplace that you can access for free. These large datasets are available for anyone to analyze the data without requiring the data to be downloaded or stored.
- Predict and Extract – Learn to use pre-trained models for predictions: This hands-on tutorial will walk you through how to use pre-trained model for predicting and feature extraction using the full Imagenet dataset.
AWS Deep Learning AMIs
AWS offers Amazon Machine Images (AMIs) for use on Amazon EC2 for quick deployment of an infrastructure needed to start your deep learning journey. The AWS Deep Learning AMIs are pre-configured with popular deep learning frameworks built using Amazon EC2 instances on Amazon Linux, and Ubuntu that can be launched for AI targeted solutions and models. The deep learning frameworks supported and pre-configured on the deep learning AMI are:
- Apache MXNet
- Microsoft Cognitive Toolkit (CNTK)
Additionally, the AWS Deep Learning AMIs install preconfigured libraries for Jupyter notebooks with Python 2.7/3.4, AWS SDK for Python, and other data science related python packages and dependencies. The AMIs also come with NVIDIA CUDA and NVIDIA CUDA Deep Neural Network (cuDNN) libraries preinstalled with all the supported deep learning frameworks and the Intel Math Kernel Library is installed for Apache MXNet framework. You can launch any of the Deep Learning AMIs by visiting the AWS Marketplace using the Try the Deep Learning AMIs link.
It is a great time to dive into Deep Learning. You can accelerate your work in deep learning by using the AWS Deep Learning AMIs running on the AWS cloud to get your deep learning environment running quickly or get started learning more about Deep Learning on AWS with MXNet using the AWS self-service resources. Of course, you can learn even more information about Deep Learning, Machine Learning, and Artificial Intelligence on AWS by reviewing the AWS Deep Learning page, the Amazon AI product page, and the AWS AI Blog.
May the Deep Learning Force be with you all.