What is Deep Learning, Anyway? As machine learning has gained significant notoriety for its wide-spread use across an immense number of applications, from retailers targeting products/marketing to individual consumers to high-frequency trading and quantitative models revolutionizing modern finance, and not to mention the seemingly constant media attention it gets in polemics surrounding privacy, user data,
While running an experiment, waiting for data is often the most challenging period as you are likely to get impatient. All you want during that period is for the A/B test to end as quickly as possible so you can go in a full-scale execution mode. And, the anxiety adds up when you don’t know
Business leaders have a delicate balancing act when it comes to AI. On one hand, according to O’Reilly, 85% of executives across 25 industries are tasked with either evaluating or deploying AI. On the other hand, risks and unintended consequences continue to grow, from Google search results showing offensively skewed results for “black girls”, to
Data science is a relatively new field, but it has become central in all spheres of human activity. So, why the ongoing buzz around data science? According to reports from IDC, the global ‘datasphere’ will expand to 175 Zettabytes by 2025. Now, that’s a lot of data. But, to get the best out of the
While performing statistical analysis, oftentimes, we face the dilemma about Frequentist Vs Bayesian Strategy for the problem. This choice becomes critical when working with limited-sized datasets. And, if you use one method over the other without having a fundamental understanding of the assumptions and limitations of the two approaches, then you could increase your chance
With the increasing amount of data generated and the evolution in the field of analytics, Data Science has turned out to be a necessity for companies to stay in the game. This has led to an increase in the demand for data scientists in various organizations to make sense of their vast amount of data.
Disruptive technologies have already begun to change the face of the education industry. The inclusion of emerging tech trends in the Edtech mobile application development process brings forth new and exciting learning opportunities for students and working professionals.
There are 3 technical reasons why the data driven / quantitative/statistical/machine learning approaches that are utterly hopeless and futile efforts, at least when it comes to language understanding.
Machine learning is important at the data-level — where we use our sensory inputs to recognize patterns and cognize of first-level objects— but what we know is a lot more and most of what matters is knowledge that is NOT learned but is knowledge that is acquired either by discovery or deduction or by being told.