We maintain the Liip Data Science Stack to help you orient yourself in a highly crowded area. Thats how we want to help you find the right tools in one place. Because we love open source, we have sorted these tools to the top. Enjoy browsing the website or if you are a geek just download the JSON file.
Often just knowing about a myriad of tools won't help you much if you can't connect them to the business question. Don't worry - you are not lost. Our team will help you to select the right approach and methodology for your question. Transform your data into insights and action starting from today.
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Where does your data usually come from? For us, it's mainly websites and apps with sophisticated event tracking. Yet for some projects the data has to be scraped, comes from social media outlets or comes from IoT devices.
How can we initially clean or transform the data? How and where can we store the logs that those events create? Also from where do we also take additional valuable data?
What options are out there to store the data? How can we search through it? How can we connect big data sources like Hadoop efficiently with existing applications?
Which stats packages are available to analyze the data? Which frameworks are out there to do machine learning, deep learning, computer vision, natural language processing?
What happens with the results? What options do we have to visually communicate them? How do we turn those visualizations into dashboards or whole applications? Which additional ways of communicating with the user beside reports/emails are out there?
What solutions are out there that try to integrate the data sourcing, data storage, analysis and visualization in one package? What solutions BI solutions are out there for big data? Are there platforms/solutions that offer more of a flexible data-scientist approach?
Learn how to build and train a deep learning network to recognize numbers (MNIST),how to convert it in the CoreML format to then deploy it on your iPhoneX and make it recognize numbers in realtime!
After learning about the dirty tricks of deep learning for computer vision in part 1 of the blog post series, now we finally write some code to train an existing resnet50 network to distinguis llamas from oryxes. Learn two tricks that allow us to do deep learning with only 100 images.
In this series of blog posts I will show you how to build a "zoo-pokedex app". That's an app that will tell different animals apart in a zoo. This blog post sells the idea of the app and demystifies the two most important basic concepts behind deep learning for image recognition.
Learn how to combine automatic speech recognition (ASR) with text to speech solutions (TTS) in a simple hands free recipe assistant prototype that we've build in an innoday at Liip. Part three of three provides the code and shows how we put everything together into a small flask socket.io prototype.
Learn how to combine automatic speech recognition (ASR) with text to speech solutions (TTS) in a simple hands free recipe assistant prototype that we've build in an innoday at Liip. Part one of two, provides an exhaustive overview of commercial and open source TTS solutions.
I recently had the honor to be invited on a small conference about the future of web development recently. I want to share my insights with you in this quick review of the talks, including insights on progressive web apps, meteor.js, microsoft azure and the data science stack.
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