Since I have promised an under 1 min reading time, let me deliver the punchline upfront — similarity between two objects is most commonly arrived at by the cosine of the angle, cos(θ), between the two corresponding vectors. The values range from -1 (opposite) to +1(the same).
The mathematical representation for cosine similarity, cos(θ), is:
‘If you cannot measure it, you cannot manage it’.
Whilst there are many metrics available to evaluate a Classification ML model, in this post, I am going to focus on the ones I have seen being used most frequently.
Confusion matrix, also referred to as error matrix is usually used to describe the performance of a classification model against a test dataset for which the values are known.
It can be best visualized as the following:
According to a survey conducted by Drift earlier this year, 15% of consumers have communicated with business via a chatbot in the last 12 months, and 47% of consumers would buy something from a chatbot according to Hubspot (2017).
With AI & Machine Learning advancing fast, one of the areas that will experience disruption in Enterprise ecosystem, is the Customer channel — with Chatbots.
With consumers already active on the popular messaging channels like Facebook, Twitter, Skype, Kik, Telegram, Whatsapp etc (depending on the geography), Chatbots are the obvious addition for Enterprises, to the traditional customer interaction channels of Physical…
Statista has it at $60 Billion by 2025, the McKinsey Global Institute puts its between $644 Million and $126 Billion by 2025 and PwC predicts it to be $15.7 Trillion by 2030. There are many such predictions, each with a different number, but the common element is that each of these numbers for the size of the global Artificial Intelligence market is profanely high.
However, with Artificial Intelligence since the potential for a positive impact is life altering — fact is so is the associated risk. …
On 5th of Jan this year, an AI model for the first time outperformed humans in reading comprehension. The SLQA+ (ensemble) model from Alibaba recorded an Exact Match score of 82.44 against the human score of 82.304 , on the SQuAD dataset.
“Handle them carefully, for words have more power than atom bombs.” — Pearl Strachan Hurd
I am fairly sure, that when Pearl Strachan said those words, she did not have text mining & analytics in mind, but nevertheless her words still ring true, especially in today’s world of conversational AI & NLP ( Natural Language Processing)!
The interest in conversational bots (chat bots) and digital virtual assistants, is growing and growing fast. Enterprises are increasingly getting eager to explore bot-enabling their business processes. Though, as with all fields of AI — conversational AI is evolving at a rapid pace, and…
If one was to go through the technology roadmap of any organization worth its salt, amongst other things, the presence of these two alphabets — ‘AI’, is almost a certainty. The other phrases associated with AI ( artificial intelligence) you are most likely to find are Machine Learning and Deep Learning.
In some cases these phrases get used interchangebly — in most cases, not entirely accurately.
So here is a quick post to help explain some of the terms a little better.
Although the title of the post says AI vs Machine learning vs Deep Learning, actually it should read…