AI Resources

AI learning resources and AI tools to accelerate your AI learning journey.

We have been asked by many how to go about learning AI and Data Science, and especially what do they need to know if they want to get into our AI Apprenticeship Programme. We have compiled a list of useful resources and some advise.

You may attend AI Singapore's AI For Everyone which is a 3-hour free seminar to provide you with a broad perspective and understanding about AI. 

You can hone your Python and/or R skills due to its popularity and versatility. There are many excellent online resources available to get started on your learning journey.

A great start will be to read and watch the excellent and legendary "Introduction to Statistical Learning" series by Hastie and Tibshirani. You can find out more here.

For those who prefer interactive hands-on learning sessions, subscribe to DataCamp and complete the Data Scientist with R career track.

Depending on your personal learning style, you may:

  1. Read a book such as Hands-on Machine Learning with Scikit-Learn and Tensorflow by Geron.
  2. Subscribe to DataCamp or sign up for AI Singapore's AI for Industry (AI4I) course which will walk you through DataCamp's Data Scientist with Python career track.

Familiarise yourself with Keras for deep learning (there is more to AI than Deep Learning, but this is a good start). Once you are comfortable, you can explore lower-level DL libraries such as Tensorflow, PyTorch or Microsoft Cognitive Toolkit.

No! In our AI Apprenticeship Programme (AIAP), we expect our AI apprentices to learn the following:

  1. SQL
  2. Apache Spark as a data platform for your AI/ML workloads
  3. Azure (or AWS, GCP) to build data products in the cloud.
  4. Container technologies such as Docker and/or Kubernetes to package, distribute and operationalize AI/ML workloads
  5. Manage a project with Git on GitHub, Bitbucket etc.!

A university level introductory Math and Statistics module would be recommended.

For the more advanced students, concentrate on Numerical Linear Algebra and Statistical Computing because these power AI libraries today. Greenbaum and Chartier's textbook Numerical Methods is a nice theoretical introduction.

For practical implementation, familiarise with the Tensorflow and Pytorch AND ALSO low-level Numpy, Scipy libraries. If you're at a more advanced level experiment with Numba and Cython more optimising how fast code runs.

Deep Learning is a subset of Machine Learning (or Statistical learning as Hastie and Tibshirani prefers to call it), and Machine Learning itself is a subset of a broader field of science called Artificial Intelligence. See the diagram below.


In addition, Pedro Domingos book - The Master Algorithm - describes the five (5) tribes of AI and their master algorithm:

  • Symbolists have inverse deduction
  • Connectionists have backpropagation
  • Evolutionaries have genetic programming
  • Bayesians have probabilistic inference
  • Analogizers have support vector machines


For a complete picture and technical introduction to AI see Artificial Intelligence: A Modern Approach by the gurus Peter Norvig and Stuart Russel.


No projects from the office or idea what to build? Go to your local community and grassroots organization and offer to work on a project to help them build something! Until you build a real-world project, you will never experience the non-technical issues on the ground.


Deep Learning – A Practitioner’s Approach

Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they even get very far. How can machine learning, especially deep nural networks, make a real difference in your organization? This hands-on guide note only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks.

This book provides the fundamentals of deep learning (tuning, paralleization, vectoriztion, and building pipelines) that are valid for any library before introducing the open source DeepLearning4J(DL4J) library for developing production-class workflows. Through real-world examples, you’ll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Haddop with DL4J.

Laurence Liew

Data mining with Rattle and R

Learning R may be difficult for non-programmers. However, there are many GUI R tools which helps make the process easier. Rattle is a robust GUI-based tool for data-mining specifically.


Introduction to Machine Learning with Python

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.

You will learn the steps necessary to create a successful machine learning application with Python and the scikit-learn library. The authors focus on the practical aspects of using machine learning algorithms, rather than math behind them. Familiarity with the numpy and matplotlib libraries will help you get even more from this book.


10 Gradient Descent Optimisation Algorithms + Cheat Sheet

Gradient descent is an optimisation method for finding the minimum of a function. It is commonly used in deep learning models to update the weights of the neural network through backpropagation.

In this post, I will summarise the common gradient descent optimisation algorithms used in popular deep learning frameworks (e.g. TensorFlow, Keras, PyTorch, Caffe). The purpose of this post is to make it easy to read and digest (using consistent nomenclature) since there aren’t many of such summaries out there, and as a cheat sheet if you want to implement them from scratch.


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