A survey of cross-lingual embedding models

A survey of cross-li...

In past blog posts, we discussed different models, objective functions, and hyperparameter choices that allow us to learn accurate word embeddings. However, these models are generally restricted to capture representations of words in the language they were trained on. The availability of resources, training data, and benchmarks in English leads to a disproportionate focus on […]

Cognitive Machine Learning: Prologue

Cognitive Machine Le...

Sources of inspiration is one thing we do not lack in machine learning. This is what, for me at least, makes  machine learning research such a rewarding and exciting area to work in. We gain inspiration from our traditional neighbors in statistics, signal processing and control engineering, information theory and statistical physics. But our fortune continues, and we […]

Handwritten digits recognition using Tensorflow with Python

Handwritten digits r...

The progress in technology that has happened over the last 10 years is unbelievable. Every corner of the world is using the top most technologies to improve existing products while also conducting immense research into inventing products that make the world the best place to live. Some of these are the Amazon just walk out […]

The future of Machine Learning lies in its (human) past

The future of Machin...

Superficially different in goals and approach, two recent algorithmic advances, Bayesian Program Learning and Galileo, are examples of one of the most interesting and powerful new trends in data analysis. It also happens to be the oldest one. Bayesian Program Learning (BPL) is deservedly one of the most discussed modeling strategies of recent times, matching […]

Wading into Deep Learning, a 30 minute query

Wading into Deep Lea...

In this interview, Jonathan Schwarz of Google DeepMind shares insight on Deep Learning projects. He offers tips and advice for the those interested in DL, and explains whether DL projects relate to other data driven projects? He comments on effective team size, software, frameworks, common mistakes, resources for learning, and more all under 30 minutes. Have a good lunch! Jonathan […]

Cognitive Machine Learning (2): Uncertain Thoughts

Cognitive Machine Le...

She pined in thought,  And with a green and yellow melancholy She sat like Patience on a monument,  Smiling at grief. Was not this love indeed? [King Lear, Act 2, Scene 4, Line 117]   In King Lear, Shakespeare stirs a sense of self-consciousness by invoking Patience, sitting up high; isolated in her thoughts; pining; reflecting in silence. It […]

Transfer Learning – Machine Learning’s Next Frontier

Transfer Learning &#...

Table of contents: What is Transfer Learning? Why Transfer Learning Now? A Definition of Transfer Learning Transfer Learning Scenarios Applications of Transfer Learning Learning from simulations Adapting to new domains Transferring knowledge across languages Transfer Learning Methods Using pre-trained CNN features Learning domain-invariant representations Making representations more similar Confusing domains Related Research Areas Semi-supervised learning […]

Cognitive Machine Learning (1): Learning to Explain

Cognitive Machine Le...

Above is an image of the Zaamenkomst panel: one of the best remaining exemplars of rock art from the San people of Southern Africa. As soon as you see it, you are inevitably herded, like the eland in the scene, through a series of thoughts. Does it have a meaning?  Why are the eland running? What do the white lines coming […]

Twelve types of Artificial Intelligence (AI) problems

Twelve types of Arti...

Background – How many cats does it take to identify a Cat? In this article, I cover the 12 types of AI problems i.e. I address the question : in which scenarios should you use Artificial Intelligence (AI)?  We cover this space in the  Enterprise AI course Some background: Recently, I conducted a strategy workshop for a group […]