A short while ago, we were happy to receive the news that ML6 won the 2020 ‘AI Innovator of the Year’ award in the prestigious Data News Awards for Excellence. This award recognizes IT companies that set the trend in creating innovation and adopting artificial intelligence technologies. The jury specifically recognized our ability to innovate together with our clients, which is something we are very proud of.
To celebrate our award, we would like to share some insights on how we foster innovation at ML6. Everyone at ML6 works hard every day to make sure that we stay at the forefront of innovation and create value with our clients, and while it’s hard to capture this spirit into words, we’ll try our best. To limit the scope of this post, we’ll mostly focus on our delivery team, but we’re happy to talk to anyone who would like to know more! Read More
Daily Archives: January 5, 2021
Finding the balance between edge AI vs. cloud AI
AI at the edge allows real-time machine learning through localized processing, allowing for immediate data processing, detailed security and heightened customer experience. At the same time, many enterprises are looking to push AI into the cloud, which can reduce barriers to implementation, improve knowledge sharing and support larger models. The path forward lies in finding a balance that takes advantage of cloud and edge strengths.
…In a perfect world, we’d centralize all workloads in the cloud for simplicity and scale, however, factors such as latency, bandwidth, autonomy, security and privacy are necessitating more AI models to be deployed at the edge, proximal to the data source. Read More
Introduction to Linear Algebra for Applied Machine Learning with Python
Linear algebra is to machine learning as flour to bakery: every machine learning model is based in linear algebra, as every cake is based in flour. It is not the only ingredient, of course. Machine learning models need vector calculus, probability, and optimization, as cakes need sugar, eggs, and butter. Applied machine learning, like bakery, is essentially about combining these mathematical ingredients in clever ways to create useful (tasty?) models.
This document contains introductory level linear algebra notes for applied machine learning. It is meant as a reference rather than a comprehensive review. … The notes are based on a series of (mostly) freely available textbooks, video lectures, and classes I’ve read, watched and taken in the past. If you want to obtain a deeper understanding or to find exercises for each topic, you may want to consult those sources directly. Read More
Interesting AI papers published in 2020
A curated list of AI papers by Ajit Jaokar, based on his research and teaching, divided into Core, ones influencing how AI algorithms could develop in future, and Interesting, based on his interests. Read More
7 popular activation functions you should know in Deep Learning and how to use them with Keras and TensorFlow 2
In artificial neural networks (ANNs), the activation function is a mathematical “gate” in between the input feeding the current neuron and its output going to the next layer [1].
The activation functions are at the very core of Deep Learning. They determine the output of a model, its accuracy, and computational efficiency. In some cases, activation functions have a major effect on the model’s ability to converge and the convergence speed.
In this article, you’ll learn seven of themost popular activation functions in Deep Learning — Sigmoid, Tanh, ReLU, Leaky ReLU, PReLU, ELU, and SELU — and how to use them with Keras and TensorFlow 2. Read More
Five Strategies for Putting AI at the Center of Digital Transformation
Across industries, companies are applying artificial intelligence to their businesses, with mixed results. “What separates the AI projects that succeed from the ones that don’t often has to do with the business strategies organizations follow when applying AI,” writes Wharton professor of operations, information and decisions Kartik Hosanagar in this opinion piece. Hosanagar is faculty director of Wharton AI for Business, a new Analytics at Wharton initiative that will support students through research, curriculum, and experiential learning to investigate AI applications. He also designed and instructs Wharton Online’s Artificial Intelligence for Business course. Read More
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