About Me

James Le is a Software Developer with experiences in Product Management and Data Analytics. He played a pivotal role in the operation of a start-up organization at Denison University.

The Ten Statistical Techniques Data Scientists Need to Master

Regardless of where you stand on the matter of Data Science sexiness, it’s simply impossible to ignore the continuing importance of data, and our ability to analyze, organize, and contextualize it. The role is here to stay, but unquestionably, the specifics of what a Data Scientist does will evolve. With technologies like Machine Learning becoming ever-more common place, and emerging fields like Deep Learning gaining significant traction amongst researchers and engineers, Data Scientists continue to ride the crest of an incredible wave of innovation and technological progress.

The Five Computer Vision Techniques That Will Change How You See The World

Computer Vision is one of the hottest research fields within Deep Learning at the moment. As Computer Vision represents a relative understanding of visual environments and their contexts, many scientists believe the field paves the way towards Artificial General Intelligence due to its cross-domain mastery. Why study Computer Vision? The most obvious answer is that there’s a fast-growing collection of useful applications derived from this field of study. Here are the 5 major computer vision techniques as well as major deep learning models and applications using each of them. They can help a computer extract, analyze, and understand useful information from a single or a sequence of images. 

The Ten Algorithms Machine Learning Engineers Need to Know

The field of machine learning / artificial intelligence has increasingly gained more popularity in the past couple of years. As Big Data is the hottest trend in the tech industry at the moment, machine learning is incredibly powerful to make predictions or calculated suggestions based on large amounts of data. Some of the most common examples of machine learning are Netflix’s algorithms to make movie suggestions based on movies you have watched in the past or Amazon’s algorithms that recommend books based on books you have bought before.

A Tour of the Top 10 Algorithms for Machine Learning Newbies

In machine learning, it states that no one algorithm works best for every problem and it’s especially relevant for supervised learning. For example, you can’t say that neural networks are always better than decision trees or vice-versa. As a result, you should try many different algorithms for your problem, while using a hold-out “test set” of data to evaluate performance and select the winner.

Sixteen useful Advices for Aspiring Data Scientists

Data Scientists at Work displays how some of the world’s top data scientists work across a dizzyingly wide variety of industries and applications — each leveraging her own blend of domain expertise, statistics, and computer science to create tremendous value and impact.

Twelve Useful Things to Know About Machine Learning

As more data becomes available, more ambitious problems can be tackled. As a result, machine learning is widely used in computer sincere and other fields. However, developing successful machine learning applications requires a substantial amount of “black art” that is hard to find in textbooks.

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