algorithm


Python: Solved Interview Ques on Algorithms, Data Structures

@machinelearnbot

Welcome to the course "Python: Solved Interview Questions on Algorithms and Data structures". We would have observed the fact that though most of us are developers, only few would get a chance to work on certain advanced programming stuff like Data Structures, Linked Lists, Trees. The rest of us get to spend time in Bug fixing, resolving Maintenance issues during our work hours. Though this work doesn't help us much in improving our learning curve, it certainly feeds us and our families. So, Keeping this in mind, at the work place, We don't have any option but to work honestly.


5 predictions for the future of machine learning

#artificialintelligence

There are countless articles and books on the future of machine learning. Today, we'll keep the discussion down-to-earth with five near-term predictions: In only a few years, machine learning will become part of nearly every software application. Think of how well your TV streaming service knows what to recommend. Expect this level of personalization to become ubiquitous and improve the customer experience everywhere. As machine learning becomes increasingly valuable and the technology matures, more businesses will start using the cloud to offer machine learning as a service (MLaaS).


As machine learning evolves, we need to update the definition of 'data scientist'

#artificialintelligence

In the early days of machine learning, hiring good statisticians was the key challenge for AI projects. Now, machine learning has evolved from its early focus on statistics to more emphasis on computation. As the process of building algorithms has become simpler and the applications for AI technology have grown, human resources professionals in AI face a new challenge. Not only are data scientists in short supply, but what makes a successful data scientist has changed. As recently as six years ago, there were minimal differences between statistical models (usually logistic regressions) and neural networks.


How to evaluate Recommender Systems – Carlos Pinela – Medium

#artificialintelligence

We have seen a variety of Recommender Systems. But we left an important issue aside: How do we evaluate RecSys? Before answering that question per se, I want to make emphasys on something. Using just one error metric can give us a limited view of how these systems work. We should always try to evaluate with different methods our models, almost as picky as your ex, but prorizing quick iteration with the lowest cost possible.


This Algorithm Predicts Who Will Die In 'Game Of Thrones' Season 8

#artificialintelligence

A data scientist has designed an algorithm that predicts which Game of Thrones characters are most likely to die in season 8. The final season of Game of Thrones may be a year away, but a lot of fans are left wondering what to expect and more specifically, which characters are going to die. Leave it up to a numbers and science guy to figure that one out. Taylor Larkin, a data scientist at Boston-based DataRoot, went through an extensive Thrones wiki database to analyze the traits of nearly 2,000 characters. Using automated machine learning, Larkin didn't cut any corners and factored in everything from gender, age, house, family lineage and more.


Artificial Intelligence Projects with Python-HandsOn: 2-in-1

@machinelearnbot

Artificial Intelligence is one of the hottest fields in computer science right now and has taken the world by storm as a major field of research and development. Python has surfaced as a dominant language in AI/ML programming because of its simplicity and flexibility, as well as its great support for open source libraries such as Scikit-learn, Keras, spaCy, and TensorFlow. If you're a Python developer who wants to take first steps in the world of artificial intelligent solutions using easy-to-follow projects, then go for this learning path. This comprehensive 2-in-1 course is designed to teach you the fundamentals of deep learning and use them to build intelligent systems. You will solve real-world problems such as face detection, handwriting recognition, and more.


Competition: Explaining black box machine learning models

@machinelearnbot

The Explainable Machine Learning Challenge is a collaboration between Google, FICO and academics at Berkeley, Oxford, Imperial, UC Irvine and MIT, to generate new research in the area of algorithmic explainability. Teams will be challenged to create machine learning models with both high accuracy and explainability; they will use a real-world financial dataset provided by FICO. Designers and end users of machine learning algorithms will both benefit from more interpretable and explainable algorithms. Machine learning model designers will benefit from Model explanations, written explanations describing the functioning of a trained model. These might include information about which variables or examples are particularly important, they might explain the logic used by an algorithm, and/or characterize input/output relationships between variables and predictions.


The AI Doctor Will See You Now

#artificialintelligence

Machine-learning algorithms accomplish tasks by training on a set of data, rather than being programmed by humans. Armed with the knowledge of what worked before, the system instructs the implant to stimulate users' brains to interrupt a seizure at its onset. The innovation is part of a larger phenomenon that has big implications for how we identify and treat disease: the introduction of artificial intelligence to consumer and clinical electronics. As machines learn from at times millions of humans, doctors are gaining the ability to better identify disease and even predict it before it becomes catastrophic. As in every other area of human endeavor, the introduction of AI to medicine comes with challenges.


Uncovering Anxious Deep Learning for Ease Vinod Sharma's Blog

#artificialintelligence

Deep Learning is an algorithm which has no theoretical limitations of what it can learn; the more data you give and the more computational time you give, the better it is – Sir Geoffrey Hinton (Google). The true challenge to Artificial Intelligence is to prove and solve the tasks that are easy for human to perform but hard to describe formally. Problems that we solve intuitively, that feel automatic, like recognizing spoken words or faces in images. In deep learning this is the task we try to solve at AILabPage research. At the same time I also claim It is absolutely wrong to call Deep Learning as Machine Learning (in my opinion).


The AI Doctor Will See You Now

WSJ.com: WSJD - Technology

Machine-learning algorithms accomplish tasks by training on a set of data, rather than being programmed by humans. Armed with the knowledge of what worked before, the system instructs the implant to stimulate users' brains to interrupt a seizure at its onset. The innovation is part of a larger phenomenon that has big implications for how we identify and treat disease: the introduction of artificial intelligence to consumer and clinical electronics. As machines learn from at times millions of humans, doctors are gaining the ability to better identify disease and even predict it before it becomes catastrophic. As in every other area of human endeavor, the introduction of AI to medicine comes with challenges.