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.
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 science still carries the aura of a new field. Most of its components — statistics, software development, evidence-based problem solving, and so on — descend directly from well-established, even old fields, but data science seems to be a fresh assemblage of these pieces into something that is new. The core of data science doesn’t concern itself with specific database implementations or programming languages, even if these are indispensable to practitioners. The core is the interplay between data content, the goals of a given project, and the data-analytic methods used to achieve those goals.
Demystifying Data Science, a free conference for aspiring data scientists and data-curious business leaders, was designed to provide insight on the training, tools, and career paths of data scientists, the conference was fully interactive, featuring real-time chat, worldwide Q&A, and polling. 14 speakers presented live before taking questions submitted via the real-time conference chat feature. The talks cover a wide range of topics: from showcasing your work to connecting with data leaders, from telling a persuasive data story to debugging myths in data science.
In an algorithm design there is no one 'silver bullet' that is a cure for all computation problems. Different problems require the use of different kinds of techniques. A good programmer uses all these techniques based on the type of problem. In this blog post, I am going to cover 2 fundamental algorithm design principles: greedy algorithms and dynamic programming. A greedy algorithm always makes the choice that seems to be the best at that moment. The core idea of Dynamic Programming is to avoid repeated work by remembering partial results and this concept finds it application in a lot of real life situations.
In computer science, divide and conquer is an algorithm design paradigm based on multi-branched recursion. A divide and conquer algorithm works by recursively breaking down a problem into two or more sub-problems of the same or related type, until these become simple enough to be solved directly. A typical Divide and Conquer algorithm solves a problem using following three steps: Divide: Break the given problem into sub-problems of same type. Conquer: Recursively solve these sub-problems. Combine: Appropriately combine the answers.