Self-driving car technologies have been in development for many decades, but breakthroughs were often mostly in recent years. Most often than not self-driving cars have proven to be much safer than human drivers, and automotive companies and other tech companies are investing billions to bring the technology into the real world.
Plentiful high-quality data is the key to great deep learning models. But good data doesn’t come easy, and that scarcity can impede the development of a good model. It’s relatively easy for a model to identify things from an image when everything is ‘Just Right’ —the correct illumination and zoom level, the right perspective etc. But when the images are not so ideal, the model struggles to give a reasonable prediction. So typically, one would want to train a network with images which are ‘not so ideal’ to help it predict better. But how do you get such data? Well, you basically fake it by taking a regular image and using data augmentation.
For the past 3 years, I have heard a lot of buzz about docker containers. I wanted to figure out about this technology & how it could help for a productive developer or data scientist.
I tried to convey my findings through this blog so you don’t need to parse all the information out there. Let’s get started.
Docker is an open-source project based on Linux containers. It uses Linux Kernel features like namespaces and control groups to create containers on top of an operating system. Docker is a tool designed to make easier to create, deploy and run applications by using containers.
We can think docker as lightweight virtual machines that contain everything you need to run an application. Even biggies like Google, Amazon, VMware have built services to support it. That’s all you need to know about docker for now.
ChatBots have been evolving for almost 3 years now. However, by 2018, ChatBots stand at a cusp, with the technology and usage, ready to take off in an exponential way; powered by AI, NLP, and Analytics. ChatBots are intended to provide end users with a Cognitive, Conversational and Customized Interface to get their job done.
Many companies, especially those in BFSI and Legal sectors, deal with a large volume of handwritten and scanned documents. It is difficult to easily use the granular information in these documents to perform an analysis or even browse through the documents in a convenient manner. A simple classification of the documents into meaningful bins or folders would make it a lot easier to leverage the information within the documents.
Banking is re-inventing itself as it always has
“Help us build the kind of bank you want to use” – That’s what Monzo says to its customers. Monzo is a UK based banking app with no usage fees, no branches, no mortgages and zero charges for spending abroad. While few of the banks in the UK might have a similar offer, but where Monzo has gained grounds is how it emotionally connects to its users. It auto-budgets your expenses, highlights where you are spending more, helps in finding the best deal e.g. it puts the money in the best savings accounts. They have formed a partnership with Google and Amazon to ensure real-time banking. For generations of users who spend a considerable amount of their time on their smartphones, it’s more than a conventional bank. Not surprising, they are termed as the new “wonder kid” in the banking industry and are aiming to have a billion-user base in next 5 years (ref: www.thegaurdian.com).
Forecasting demand for new product launches has been a major challenge for industries and cost of error has been high. Under predict demand and you lose on potential sales, overpredict them and there is excess inventory to take care of. Multiple research suggests that new product contributes to one-third of the organization sales across the various industry. Industries like Apparel Retailer or Gaming thrive on new launches and innovation, and this number can easily inflate to as high as 70%. Hence accuracy of demand forecasts has been a top priority for marketers and inventory planning teams.