Predict Herbs in C# With the Custom Vision SDK
/Just published a quick videon to show how easy it is to use the Custom Vision SDK in C# to make predictions with a Custom Vision model.
Just published a quick videon to show how easy it is to use the Custom Vision SDK in C# to make predictions with a Custom Vision model.
Since I'm starting to read more and more research papers, I thought I'd give a small rundown on where I'm finding these papers. You can find a lot available for free, and the places below are my favorite ones to go to.
Arxiv (I believe it is pronounced "archive") is the most popular place to find research papers. There are several subsections but the ones to look at are machine learning and artificial intelligence. There is just so much you find here. In fact, there's so much there's an open source version of Arxiv called Arxiv Sanity.
Yep, the main place to find code is also a great place to find collections of research papers. Here are just a few of those collections to get you started.
Papers We Love are a community of people who like to read computer science papers and then talk about what they have read. It's like a book club for computer science research. While this isn't solely for machine learning or AI there are some papers that touch on those fields.
While this repository isn't the most up-to-date having papers from NIPS in 2016, they do link out to the GitHub repositories so you can access the source code along with reading the paper.
This repository has quite a lot of papers in it. It has them broken down by category such as natural language process and reinforcement learning. This doesn't have just papers, either. There are some links to video lectures and other blogs you can go to as additional resources.
Quick tip if you want to find more more GitHub lists that are curated, there's a lot of people who have lists of topics under the awesome badge. Doing a search for github awesome
and then what you're looking for will yield some interesting results.
This is more of a search than a list of articles, but you can find a lot here. You can even create alerts on keywords or by researchers you want to follow to see what they are citing.
Reddit is always a good place to find a community in topics that you're interesting in. Machine learning and sharing interesting papers has a place there as well.
While I'm on the data science subreddit a lot, the machine learning one is great for research, projects, and discussions. Often times, on the research posts, you'll get some extra context from the comments which can be more valulable than the paper itself.
Some of the big tech companies have their own research entities, such as Google's Deep Mind, Microsoft Research, and Facebook Research. Often times, they'll publish their papers on their sites for anyone to access. Even better, sometimes they'll put out a blog post that highlights what a paper is about and will include some more feature rich graphics to go along with it that you can't always put into a research publication to help you understand what's going on in the paper.
There are some interesting journals that tailor specifically for publishing your work. Like ArXiv, all of the papers here are free to access. You have the Journal of Machine Learning Research which is specific to only machine learning topics. The Journal of Data Science which encompasses the huge field of data science, which you'll probably see a lot of statistics papers in here as well. And then there's the R Journal which has papers where the code was specifically written in R, so you may have more statistics topics in here, as well.
Hopefully, with these resources you'll be able to find research papers that will keep you busy for quite a long time.
Another video to show how to use the Custom Vision Service as part of the Microsoft Cognitive Services to predict what herb is in a photo.
Just made a very quick video to go over how to perform cross validation to evaluate your models in ML.NET. Hope you enjoy!
In trying to do more screencasts as well as blog posts I created a screencast that goes over this Wintellect post on ML.NET I did recently.
Hope you enjoy it! And please submit any feedback if you have any.
During the same week PyCon was going on, Microsoft had their annual Build conference. If you're not familiar with this conference this is where Microsoft announces a lot of new things for developers. The main focus of this year's Build was about artificial intelligence.
Also in the same way as PyCon, Microsoft records all of the sessions at Build so we're free to watch them later. With that, I present to you what I think are the top five session at Build that go over artificial intelligence. Why top five instead of top ten like in the PyCon session post? Well, there just wasn't enough to do a top ten. :)
This session goes a lot into the Cognitive Services. Not only how much it can improve your applications by incorporating them, but also how easy they are to implement.
Continuing with more information about Cognitive Services, this session goes into some of the other capabilities that were announced at Build. A nice demo they showed went over what they did with the released JFK files and how they used Cognitive Search to analyze all of that data.
This is an interesting talk that goes into the basics of data science and machine learning, but also goes into how to integrate the mindset of DevOps into data science. Doing so can help with things like testing and version control as a process to doing data science which can help reproducability and putting models into production.
This video is full of great demos that show off the power of several Cognitive Services. Although, my favorite demo is the one where they show the power of having a bot on your site.
There are quite a few deep learning videos out there, but I firmly believe that this is one of the best. The way that Seth and Chris describe the deep learning process of the neural network algorithms makes it very understandable to what's going on behind the scenes when you train a model.
So a lot of interesting things happened at build in terms of AI, mostly involving their Cognitive Services APIs. These APIs, I think, are going to really help make your apps stand out from the rest of the crowd and Microsoft looks to continue to add to them with APIs from their Cognitive Services Labs.
I'm definitely looking forward to what else they come up with.
Jonathan is a normal software developer residing in Columbia, SC. During the day he creates software using the Microsoft technology stack: by night he's doing martial arts or off learning and experiencing new things. He can also be found taking long walks around the parking lot or just sitting around reading.
Jon is a random software developer in North Carolina developing in C# for web and machine learning with ML.NET. I also post videos on YouTube.