There’s no debate that hot IT certifications are worthy add-ons for tech professionals trying to boost their job prospects. But the problem with certs is they are mostly limited to infrastructure roles and related technology products. The vendors of these products are obligated to train their customers in how to use them and offer certifications for this purpose that are arguably easy to obtain. This leaves countless tech skills for which either there are no certifications available or it just doesn’t matter to employers eager to place a value on these skills and offer extra cash to workers who acquire
Deep learning continues to be one of the hottest fields in computing, and while Google’s TensorFlow remains the most popular framework in absolute numbers, Facebook’s PyTorch has quickly earned a reputation for being easier to grasp and use.
PyTorch has taken the world of deep learning research by storm, outstripping TensorFlow as the implementation framework of choice in submitted papers for AI conferences in the past two years. With recent improvements for producing optimized models and deploying them to production, PyTorch is definitely a framework ready for use in industry as well as R&D labs.
But how to get started?
The hassles of data intake and cleaning, problems with biased models and data privacy, and difficulty finding experience and technical skills—all these ranked among the biggest challenges facing data scientists and software engineers in data-science disciplines according to a newly released survey.
Anaconda, makers of the Python distribution of the same name for scientific computing applications, conducted its 2020 State Of Data Science survey with 2,360 respondents from 100 countries, slightly less than half of those hailing from the U.S.
Despite all the advances in recent years in data science work environments, data drudgery remains a major part of
Data science is typically more of an art than a science, despite the name. You start with dirty data and an old statistical predictive model and try to do better with machine learning. Nobody checks your work or tries to improve it: If your new model fits better than the old one, you adopt it and move on to the next problem. When the data starts drifting and the model stops working, you update the model from the new dataset.
Doing data science in Kaggle is quite different. Kaggle is an online machine learning environment and community. It has
Hi. I’m Sharon Machlis at IDG Communications, here with a very quick Episode 49 of Do More With R: Color matched parentheses in RStudio.
No matter how good you are at formatting and indenting your code, if you’ve got a long and complex task, you can start losing track of which brackets and braces belong to what. Now, what we probably ought to do is refactor the code – break it up into smaller pieces and functions. But for those of us who don’t always do that . . . well, color-matching parentheses and brackets have been an RStudio requested
Executive involvement in enterprise artificial intelligence (AI) initiatives is growing rapidly and more emphasis is being placed on high-quality training data. Both C-suite ownership of AI and budgets over $500K nearly doubled in 2020 due to the COVID-19 pandemic serving as a catalyst for accelerated AI initiatives.
A key lesson learned from the pandemic is that businesses need to be ready for anything that requires a high level of business agility. It’s Darwinism at its finest as businesses that can adapt to market trends faster than their competition can become market leaders and maintain that position. Those that can’t do