Deep Learning, NLP, NMT, AI, ML
Python tutorials
DataJoy is an unbelievably fantastic way for a working data scientist to have their favorite tools at hand. I am a minimalist when it comes to being mobile, whether working on the road, traveling for leisure, and sometimes both. I do not like to keep files on my laptop and I do not, for the most part, like to worry about keeping updated applications on my laptop. I have tried as much as possible to push my life into the […]
Read more
To finish reading, please visit source site
Read more
Summary: Picking an analytic platform when first starting out in data science almost always means working with what we’re most comfortable. But as organizations grow larger there is a need for standardization and for selecting one, or a few analytic tools. Picking an analytic platform when first starting out in data science almost always means working with what we’re most comfortable. That in turn almost always means whatever we used in college (or your certificate course) be it R, […]
Read more
Beer is delicious but it is not one thing. If you disagree with the former part of the previous sentence please keep the latter in mind[1]. Think of sports, for instance. Many would agree with the blanket statement “sports are fun” but depending on what you have in mind two people can easily have opposite reactions to being presented the opportunity to play ping-pong. Sports are not one thing, music is not one thing, and neither is beer. Presented with […]
Read more
Python, R and SAS are the three most popular languages in data science. If you are new to the world of data science and aren’t experienced in either of these languages, it makes sense to be unsure of whether to learn R, SAS or Python. Don’t fret, by the time you’re done reading this article, you will know without a doubt which language is the right one for you. Overview R – R is the lingua franca of statistics. It is a […]
Read more
Summary: The largest companies utilizing the most data science resources are moving rapidly toward more integrated advanced analytic platforms. The features they are demanding are evolving to promote speed, simplicity, quality, and manageability. This has some interesting implications for open source R and Python widely taught in schools but significantly less necessary with these more sophisticated platforms. We continue to be dazzled, and perhaps rightly so, by the advances in deep learning and question answering machines like Watson. And […]
Read more
Summary: Someone had to say it. In my opinion R is not the best way to learn data science and not the best way to practice it either. More and more large employers agree. Someone had to say it. I know this will be controversial and I welcome your comments but in my opinion R is not the best way to learn data science and not the best way to practice it either. Why Should We Care What […]
Read moreAccording to the IT programming trend, Java is currently more popular than other programming languages in terms of number of jobs, number of existing Java developers and overall usage statics in IT compared to Python. According to the latest usage statistics posted on a popular Technology Survey site, Java is being used by 3.0% websites as a server-side programming language, whereas only 0.2% of websites use Python. However, all the recent reports have highlighted that the usage and popularity of Python […]
Read moreIntroduction Supervised Contrastive Learning paper claims a big deal about supervised learning and cross-entropy loss vs supervised contrastive loss for better image representation and classification tasks. Let’s go in-depth in this paper what is about. Claim actually close to 1% improvement on image net data set¹. Architecture wise, its a very simple network resnet 50 having a 128-dimensional head. If you want you can add a few more layers as well. Architecture and training process from the paper Codeself.encoder = […]
Read more
What is VirtualEnv? The virtualenv tool creates an isolated Python environment (in the form of a directory) that is completely separate from the system-wide Python environment. What this really means is that any settings, 3rd-party packages, etc. from the system-wide environment do not appear in the virtual environment, so it’s almost like you have a clean Python install. This is useful for when you want to have a clean-slate for your projects. Let’s say you have boto version 2.7.0 installed […]
Read more