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.
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 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.
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.
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.
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.
Natural Language Processing (NLP) is a field at the intersection of computer science, artificial intelligence, and linguistics. The goal is for computers to process or “understand” natural language in order to perform tasks that are useful, such as Performing Tasks, Language Translation, and Question Answering. It is certainly one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. This 2-part series shares the 7 major NLP techniques as well as major deep learning models and applications using each of them.
Deep Learning requires a lot of computations. It typically involves neural network(s) with many nodes, and every node has many connections — which must be updated constantly during the learning. As the deep learning and AI fields have been moving extremely fast in the last few years, we’ve also seen the introduction of many deep learning frameworks. Deep learning frameworks are created with the goal to run deep learning systems efficiently on GPUs.
In part 1, we introduced the field of Natural Language Processing (NLP) and the deep learning movement that’s powered it was introduced. We also walked you through 3 critical concepts in NLP: text embeddings (vector representations of strings), machine translation (using neural networks to translate languages), and dialogue & conversation (tech that can hold conversations with humans in real time). In part 2, we’ll cover 4 other important NLP techniques that you should pay attention to in order to keep up with the fast growing pace of this research field.
Data scientists are often tasked with analyzing data to help the business, and this requires a level of business acumen. It’ll be extremely valuable for any aspiring data scientists to learn data mining — the process where one structures the raw data and formulates or recognizes the various patterns in the data through the mathematical and computational algorithms. This helps to generate new information and unlock various insights. Here are 10 mining techniques that I any data scientists should learn to be more effective while handling big datasets.