Recently I gave a talk at PyData Seattle about how to ramp up your data science skills by borrowing tips and tricks from the developer community. These suggestions will help you become a more proficient data scientist who is loved by your team members and stakeholders.
This post is broken up into five parts including:
- History and controversy of the 10x developer.
- Project design.
- Code design.
- Tools for the job.
- Productionizing model.
Earlier this week we introduced Face Recognition, a trainable model that is hosted on Algorithmia. This model enables you to train images of people that you want the model to recognize and then you can pass in unseen images to the model to get a prediction score.
The great thing about this algorithm is that you don’t have to have a huge dataset to get a high accuracy on the prediction scores of unseen images. The Face Recognition algorithm trains your data quickly using at least ten images of each person that you wish to train on. Read More…
Earlier this week we introduced Censorface, an algorithm that finds the faces in images and then either blurs or puts a colored box over the faces to censor them. We thought it would be fun to pair it up with some of our video processing algorithms to show how you can use different algorithms together to censor a video clip when you don’t want to run the whole video.
Maybe you have some embarrassing videos that you want to share, but don’t want anyone to know it’s you! Or maybe you have a potentially viral video that you want to post on YouTube, but you need to protect the innocent. No matter what your use case is, let’s dive into creating non-nude video clips with censored faces! Read More…
Recently, we wrote a blog post about an algorithm called Scene Detection that takes a video and returns the timestamps of scenes along with subclips that are associated with the subclip’s timestamps.
You can use this information to find appropriate scene lengths for creating video trailers or you can use the timestamps of scenes to dictate where YouTube can place advertisements so it doesn’t occur during an important scene.
Sometimes though, you want more than just the scene’s timestamps. With Python 3.4 and up you can use the statistics module to determine the average length of a scene, the variance of the data and other information to easily edit your videos or garner insights from the scene lengths. Although you can perform statistical calculations manually or by using the libraries Numpy or Pandas, in Python 3.4 and up you can easily find detailed information of your subclip data without importing a bunch of heavy libraries. Read More…
A couple of weeks ago we gave a talk at the Seattle Spark Meetup about bringing together the flexibility of Algorithmia’s deep learning algorithms and Spark’s robust data processing platform. We highlighted the strengths of both platforms and covered a basic introduction on how to integrate Algorithmia with Spark’s Streaming API. In this talk you’ll see how we went from use case idea to implementation in only a few lines of code. Read More…