AI Singapore : Past, Present, Future
In our inaugural podcast, I had the opportunity to speak…
When Machines Mimic Your Voice
Last month, in a Facebook post, Prime Minister Lee Hsien…
Intel Python’s Performance
Introduction Machine learning algorithms can take significant amounts of time…
It is Good to Learn Programming
As an economist by training, I did a lot of…
AI Singapore at PyCon Singapore 2019
Embracing AI, sharpening competitiveness, improving lives The annual PyCon Singapore…
Word Embedding, Character Embedding and Contextual Embedding in BiDAF — an Illustrated Guide
BiDAF is a popular machine learning model for Question and Answering…
What are all these terms … Data Science, Machine Learning, Artificial Intelligence?
As many of you know, I conduct training in a…
An Illustrated Guide to Bi-Directional Attention Flow (BiDAF)
This article illustrates the workings of BiDAF, an NLP model…
Illustrated: 10 CNN Architectures
A compiled visualisation of the common convolutional neural networks.
Learn and Contribute
It was with great pleasure that AI Singapore invited Aurélien…
Animated RNN, LSTM & GRU
Recurrent neural networks are a class of artificial neural networks…
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.
