Analytics has taken world by storm & It it the powerhouse for all the digital transformation happening in every industry.
- Google’s Tensorflow
- Facebook open source modules for Torch
- Amazon released DSSTNE on GitHub
- Microsoft released CNTK, its open source deep learning toolkit, on GitHub
Today we see lot of examples of Deep learning around:
- Google Translate is using deep learning and image recognition to translate not only voice but written languages as well.
- With CamFind app, simply take a picture of any object and it uses mobile visual search technology to tell you what it is. It provides fast, accurate results with no typing necessary. Snap a picture, learn more. That’s it.
- All digital assistants like Siri, Cortana, Alexa & Google Now are using deep learning for natural language processing and speech recognition
- Amazon, Netflix & Spotify are using recommendation engines using deep learning for next best offer, movies and music
- Google PlaNet can look at the photo and tell where it was taken
- DCGAN is used for enhancing and completing the human faces
- DeepStereo: Turns images from Street View into a 3D space that shows unseen views from different angles by figuring out the depth and color of each pixel
- DeepMind’s WaveNet is able to generate speech which mimics any human voice that sounds more natural than the best existing Text-to-Speech systems
- Paypal is using H2O based deep learning to prevent fraud in payments
- Minimize maintenance costs - Don’t waste money through over-cautious time bound maintenance. Only repair equipment when repairs are actually needed.
- Reduce unplanned downtime - Implement predictive maintenance to predict future equipment malfunctioning and failures and minimize the risk for unplanned disasters putting your business at risk.
- Root cause analysis - Find causes for equipment malfunctions and work with suppliers to switch-off reasons for high failure rates. Increase return on your assets.
- Efficient labor planning — no time wasted replacing/fixing equipment that doesn’t need it
- Avoid warranty cost for failure recovery – thousands of recalls in case of automakers while production loss in assembly line
TrainItalia has invested 50M euros in Internet of Things project which expects to cut maintenance costs by up to 130M euros to increase train availability and customer satisfaction.
- Increased need & desire among businesses to gain greater value from their data
- Over 80% of data/information that businesses generate and collect is unstructured or semi-structured data that need special treatment
- Typically requires mix of skills - mathematics, statistics, computer science, machine learning and most importantly business knowledge
- They need to employ the R or Python programming language to clean and remove irrelevant data
- Create algorithms to solve the business problems
- Finally effectively communicate the findings to management
Any company, in any industry, that crunches large volumes of numbers, possesses lots of operational and customer data, or can benefit from social media streams, credit data, consumer research or third-party data sets can benefit from having a data scientist or a data science team.
- Kirk D Borne of BoozAllen
- D J Patil Chief Data Scientist at White House
- Gregory Piatetsky of kdnuggets
- Vincent Granville of Analyticsbridge
- Jonathan Goldman of LinkedIn
- Ronald Van Loon
Although computers are better for data processing and making calculations, they were not able to accomplish some of the most basic human tasks, like recognizing Apple or Orange from basket of fruits, till now.
Here are a few thoughts from @dataguild on IoT as applied to Data Science. Thanks to @MacSlocum, @JonBruner and the @OReillySolid crew for a great show in San Francisco last month.
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