Explore the AI Journey

From Ideation to Launch

Written by experts in AI, machine learning, and deep learning, the Hands-On AI tutorial series covers the tools, infrastructure, and techniques to help you build an application with powerful AI capabilities.

Learn About

  • Docker* containers
  • Keras neural network API
  • TensorFlow* software library for machine intelligence
  • Caffe* deep learning framework

Recommended Skills

  • Intermediate knowledge of Python*
  • Basic linear algebra
  • Basic statistics
  • Basic probability theory
  • Familiarity with GitHub*

Anyone can follow along as we share source code and cloning environments to assist you in your app development. And nontechnical readers can gain insight and details on how to develop an AI application.

Tutorial Series

Ideation & Planning

Kickoff your project by defining a common set of goals, data sources, technology limitations, the team, and a strategic plan.

Create Applications with Powerful AI Capabilities
Familiarize yourself with the tools, infrastructure, and techniques used in artificial intelligence development through the process of building a sample application.

Ideation
Explore the creative process of turning a concept into a product. Identify your users, brainstorm and define use cases, and evaluate the feasibility of your idea.

The Anatomy of an AI Team
Understand the types of contributors you may need for your team, each role's required skills, and how to find the most appropriate person to fill that role.

Project Planning
Define the tasks required for your AI project by applying different project analysis and planning methodologies, and learn about general project management guidelines.

Technology & Infrastructure

Discover how data is collected, analyzed, preprocessed, and annotated. Explore various frameworks, learn to select the best one suited for your needs, and discover data center and cloud computing options.

Select a Deep Learning Framework
Compare and evaluate deep learning frameworks and learn about an evaluation methodology that can be applied to the selection process.

Select an AI Computing Infrastructure
Explore a variety of infrastructure options, including computing resources from Intel that can be used to for deep learning.

Augment AI with Human Intelligence Using Amazon Mechanical Turk*
Learn how crowdsourcing provides a scalable solution to human and artificial intelligence tasks using Amazon Mechanical Turk, and get tips on how to use it effectively.

Crowdsourcing Word Selection for Image Search
Apply crowdsourcing in a real pipeline and scale it to address the collection and annotation of a large set of images.

Data Annotation Techniques
Understand various data annotation techniques and protocols. Using real-world examples, learn how to apply them to your project.

Set Up a Portable Experimental Environment for Deep Learning with Docker*
Get an introduction to Docker* with steps on how to set up a portable environment and train a simple neural network.

App Development & Deployment

Take your app from prototype to working application, and prepare it for production.

TensorFlow Serving for AI API and Web App Deployment

This final article provides step-by-step instructions on how to deploy web API services incorporating emotion recognition (image processing) and music generation, including a web app server with a user interface.