Introducing adversarial examples in deep learning vision models

Source: Pixabay


We have seen the advent of state-of-the-art (SOTA) deep learning models for computer vision ever since we started getting bigger and better compute (GPUs and TPUs), more data (ImageNet etc.) and easy to use open-source software and tools (TensorFlow and PyTorch). Every year (and now every few months!) we see the next SOTA deep learning model dethrone the previous model in terms of Top-k accuracy for benchmark datasets. The following figure depicts some of the latest SOTA deep learning vision models (and doesn’t depict some like Google’s BigTransfer!).

A comprehensive hands-on guide to TensorFlow Serving


Thanks to faster compute, better storage and easy to use software, deep learning based solutions are definitely seeing the light of the day coming out from the proof-of-concept tunnel into the real-world! We are seeing widespread adoption of deep learning models across diverse domains in the industry including healthcare, finance, retail, tech, logistics, food-tech, agriculture amongst many others! Considering the fact that deep learning models are resource hungry and often compute-heavy, we need to pause for a moment and think about model inference and serving times, when consumed by end-users.

Training and performing model inference on static batches of data…


Classifying Radio-Telescope Signals from SETI with Deep Learning

Photo by Donald Giannatti on Unsplash


Welcome (or welcome back!) to the AI for social good series! In the second part, of this two-part series of articles, we will look at how Artificial intelligence (AI) coupled with the power of open-source tools and techniques like deep learning can help us further the quest for finding extra-terrestrial intelligence!

In the first part of this two-part series, we formulated our key objective and motivation behind doing this project. Briefly, we were looking at different radio-telescope signals simulated from SETI (Search for Extra-terrestrial Intelligence) Institute data. …

AI for Social Good Series — Part 2.1

Understanding Radio-Telescope Signal Data from SETI

Photo by Ambir Tolang on Unsplash


In this two-part series of articles, we will look at how Artificial intelligence (AI) coupled with the power of open-source tools and frameworks can be used to solve a very interesting problem in a non-conventional domain — the quest for finding extra-terrestrial intelligence!

Perhaps many of you are already familiar with the SETI (Search for Extra-terrestrial Intelligence) Institute, which focuses on trying to find out the existence of extra-terrestrial intelligence out in the universe and as its mission suggests, “exploring, understanding and explaining the origin and nature of life in the universe and the evolution of intelligence”. Recently, I had…

Explainable Artificial Intelligence

Interpreting Convolutional Neural Network Models built with TensorFlow

What does a deep learning model really see?


Artificial Intelligence (AI) is no longer a field restricted only to research papers and academia. Businesses and organizations across diverse domains in the industry are building large-scale applications powered by AI. The questions to think about here would be, “Do we trust decisions made by AI models?” and “How does a machine learning or deep learning model make its decisions?”.

AI for Social Good Series— Part 1

AI for Social Good — A Healthcare Case Study

Photo by Hal Gatewood on Unsplash

The content for this article has been adapted from my own article published previously in


Welcome to the AI for Social Good Series, where we will be focusing on different aspects of how Artificial Intelligence (AI) coupled with popular open-source tools, technologies and frameworks are being used for development and betterment of our society. “Health is Wealth” is perhaps a cliched quote yet very true! In this particular article, we will look at how AI can be leveraged for detecting malaria, a deadly disease and the promise of building a low-cost, yet effective and accurate open-source solution. The intent…

Data Analysis & Visualization at Scale on Semi-structured Data

Photo by Robin Pierre on Unsplash


One of the most popular and effective enterprise case-studies which leverage analytics today is log analytics. Almost every small and big organization today have multiple systems and infrastructure running day in and day out. To effectively keep their business running, organizations need to know if their infrastructure is performing to its maximum potential. This involves analyzing system and application logs and maybe even apply predictive analytics on log data. The amount of log data is typically massive, depending on the type of organizational infrastructure and applications running on it. …

A guide to the less desirable aspects of deep learning environment configurations

Photo by Kyle Hanson on Unsplash


Thanks to cheaper and bigger storage we have more data than what we had a couple of years back. We do owe our thanks to Big Data no matter how much hype it has created. However, the real MVP here is faster and better computing ,which made papers from the 1980s and 90s more relevant (LSTMs were actually invented in 1997)! We are finally able to leverage the true power of neural networks and deep learning thanks to better and faster CPUs and GPUs. …

Data Science Strategic Guide — Part 1

Take your Data Science Projects from Zero to Production


The ‘Data Science Strategic Guide — Get Smarter with Data Science’ is envisioned as a series of articles, which serve to be more of a strategic guide depicting essential challenges, pitfalls and principles to keep in mind when implementing and executing data science projects in the real-world. We will also cover how you can get maximum value from data science and artificial intelligence, by focusing on very real perspectives and staying far away from the hype. This should enable you to drive success in the industry in your own domain! …

Explainable Artificial Intelligence (Part 3)

A comprehensive guide to interpreting machine learning models


Interpreting Machine Learning models is no longer a luxury but a necessity given the rapid adoption of AI in the industry. This article in a continuation in my series of articles aimed at ‘Explainable Artificial Intelligence (XAI)’. The idea here is to cut through the hype and enable you with the tools and techniques needed to start interpreting any black box machine learning model. Following are the previous articles in the series in case you want to give them a quick skim (but are not mandatory for this article).

Dipanjan (DJ) Sarkar

Data Science Lead @Applied4Tech, @Google Developer Expert — Machine Learning, Author, Consultant, AI Advisor @Springboard, Connect:

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