Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...
Gartner predicts that the number of IoT devices will surpass 11.2 billion this year, the majority of which are in the consumer sector. The same report forecasts that the endpoint spending will exceed $2 trillion, in hardware and software combined.
Source: iotevolutionworld
12927
Over the past several years, deep learning has become the go-to technique for most AI type problems, overshadowing classical machine learning. The clear reason for this is that deep learning has repeatedly demonstrated its superior performance on a wide variety of tasks including speech, natural language, vision, and playing games.
Source: TDS 22101
The most important lessons I've learned so far... Ok, so it's actually 14 months but it didn't have the same ring to it as "1 year". I've spent that 14 months at one company, News UK. I'm now about to embark on a new journey at Deliveroo as a consumer and growth algorithms data scientist, so I thought now would be a good time to reflect on the things that I've learned during my time at News UK.
Source: TDS 13566
Deep learning emerged from that decade's explosive computational growth as a serious contender in the field, winning many important machine learning competitions. The interest has not cooled as of 2017; today, we see deep learning mentioned in every corner of machine learning.
Source: Kdnugget 15279
Deep Learning is constantly evolving at a fast pace. New techniques, tools and implementations are changing the field of Machine Learning and bringing excellent results.
Source: Kdnugget 26916
Ability to write a production-level code is one of the sought-after skills for a data scientist role- either posted explicitly or not. For a software engineer turned data scientist this may not sound like a challenging task as they might have already perfected their skill at developing production level codes and deployed into production several times.
Source: TDS 12555
I've had several conversations recently with people I know in the data science space that always start out about business and then drift to the state of data science as a whole.
Source: DataScience 16155
Deep changes are underway in how data science is practiced and successfully deployed to solve business problems and create strategic advantage. These same changes point to major changes in how data scientists will do their work. Here's why and how.
Source: DS 17133
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