By Kimberly Cook There are a lot of computer science graduates and programmers applying for programming, coding, and software development roles at startups like Uber and Netflix; big organizations like Amazon, Microsoft, and Google; and service-based companies like Infosys or Luxsoft, but many of them have no idea of what kind of programming interview questions to expect when you're applying for … Continue reading 50+ Data Structure, Algorithms & Programming Languages Interview Questions for Programmers
Month: September 2018
When Bayes, Ockham, and Shannon come together to define machine learning
By Tirthajyoti Sarkar A beautiful idea, which binds together concepts from statistics, information theory, and philosophy. Introduction It is somewhat surprising that among all the high-flying buzzwords of machine learning, we don’t hear much about the one phrase which fuses some of the core concepts of statistical learning, information theory, and natural philosophy into a single … Continue reading When Bayes, Ockham, and Shannon come together to define machine learning
50+ Data Structure, Algorithms & Programming Languages Interview Questions for Programmers
By Kimberly Cook There are a lot of computer science graduates and programmers applying for programming, coding, and software development roles at startups like Uber and Netflix; big organizations like Amazon, Microsoft, and Google; and service-based companies like Infosys or Luxsoft, but many of them have no idea of what kind of programming interview questions to expect when you're applying for … Continue reading 50+ Data Structure, Algorithms & Programming Languages Interview Questions for Programmers
When Bayes, Ockham, and Shannon come together to define machine learning
By Tirthajyoti Sarkar A beautiful idea, which binds together concepts from statistics, information theory, and philosophy. Introduction It is somewhat surprising that among all the high-flying buzzwords of machine learning, we don’t hear much about the one phrase which fuses some of the core concepts of statistical learning, information theory, and natural philosophy into a single … Continue reading When Bayes, Ockham, and Shannon come together to define machine learning
Deep Learning Framework Power Scores 2018
By Jeff Hale Who’s on top in usage, interest, and popularity? Deep learning continues to be the hottest thing in data science. Deep learning frameworks are changing rapidly. Just five years ago, none of the leaders other than Theano were even around. I wanted to find evidence for which frameworks merit attention, so I developed this power … Continue reading Deep Learning Framework Power Scores 2018
Designing the Future of Work
By Sara Ortloff Khoury At Google Cloud my job is to reimagine enterprise — the tools we build and how we design them. Traditional enterprise products don’t reflect how people work — our pain points, our tasks across the workday, our desire to stay a step ahead. They lack human-centered design. They lack AI. Before coming to Google, I spent … Continue reading Designing the Future of Work
Only Numpy: Deriving Forward Feed on Multi-Dimensional Recurrent Neural Networks (Spatial LSTM) by “Generative Image Modeling Using Spatial LSTMs”
By Jae Duk Seo Multi-Dimensional Recurrent Neural Networks, I became interested in them as soon as I heard it’s name. So today, I will attempt to tackle the network structure of Spatial LSTM introduce in this paper. “ Generative Image Modeling Using Spatial LSTMs” — by Lucas Theis. Also for today’s blog we will perform Forward Feed on 2D LSTM. Transform … Continue reading Only Numpy: Deriving Forward Feed on Multi-Dimensional Recurrent Neural Networks (Spatial LSTM) by “Generative Image Modeling Using Spatial LSTMs”
Google launches new search engine to help scientists find the datasets they need
Dataset Search could be a scientist’s best friend https://toolbox.google.com/datasetsearch By James Vincent Illustration by Alex Castro / The Verge Google’s goal has always been to organize the world’s information, and its first target was the commercial web. Now, it wants to do the same for the scientific community with a new search engine for datasets. The … Continue reading Google launches new search engine to help scientists find the datasets they need
Dopamine – Research framework for fast prototyping of reinforcement learning algorithms
By Kirti Bakshi Over the past few years, Reinforcement learning (RL) research has seen a number of significant advances and the type of progress it has made turn out to be very important, as the algorithms that yield these advances are additionally applicable for other domains, such as in robotics. Very often, developing these kinds of advances … Continue reading Dopamine – Research framework for fast prototyping of reinforcement learning algorithms
Deep Learning Framework Power Scores 2018
By Jeff Hale Who’s on top in usage, interest, and popularity? Deep learning continues to be the hottest thing in data science. Deep learning frameworks are changing rapidly. Just five years ago, none of the leaders other than Theano were even around. I wanted to find evidence for which frameworks merit attention, so I developed this power … Continue reading Deep Learning Framework Power Scores 2018