Top 5 YouTube Channels

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YouTube is a great resource to learn new development topics. It is one of my go to places if I want to learn a new topic or even to get into a specific area. However, there are a few channels that I make sure and check out if there are new videos.

Traversy Media

One of my favorite channels, Brad really has some great content. Mostly going over web technologies he definitely goes over the big three frameworks - React, Angular, and Vue. However, he also goes over some pure CSS, HTML, and JavaScript in some of his videos. He also has videos that go over career and personal development issues that I find very helpful.

Fireship.io

This channel used to be called Angular Fire and by the name you may guess what he covers - Angular and Firebase. However, he's branched out quite a bit into some other videos such as Flutter for mobile applications. He has some of the best RxJS videos that I've found very helpful.

Academind

Maximilian Schwarzmüller is one of my favorite instructors for web based technologies. He has courses in all of the current popular frameworks - Angular, React, and Vue. He also has courses in some other technologies which may be useful to learn, such as Flutter and React Native for mobile applications and AWS for serverless applications.

Corey Schafer

Corey's channel is probably the most popular in terms of Python content. He's got so many videos that whatever you need to do in Python he already has a video about it. Another great thing about this channel is that he also goes into other developer tools, such as bash and dot files which I just started watching, as well as git.

Data School

The Data School channel is the best if you want to learn data wrangling and data cleaning with pandas or machine learning with scikit-learn. You can tell he has a deep knowledge with both of these libraries.

Top 5 Machine Learning Books

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Machine learning is a vast subject and there is a lot to learn. Luckily, there are several books out there that can help us along the way. Below I list what I believe are the top 5 machine learning books that are currently out there.

5 Books To Become a Better Software Developer

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This video goes over the top five books I found to help me become a better software developer. Hope you will find it useful. If you have a book that has helped you, feel free to put it in the comments.

Books in slides:

Other books mentioned in the video:

5 Best Places to Read Research Papers

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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

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.

GitHub

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

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.

Machine Learning Papers

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.

Deep Learning Papers

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.

Google Scholar

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.

Machine Learning subreddit

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.

Tech Company Sites

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.

Specific Journal Sites

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.

Top 5 Artificial Intelligence Sessions at Build 2018

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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. :)

10 Things Developers Need to Know about Building Intelligent Apps by Noelle LaCharite

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.

The Microsoft AI platform: a State of the Union by Joseph Sirosh

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.

DevOps for AI : Deploying everywhere by Paige Bailey

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.

Microsoft AI overview for developers by Harry Shum

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.

Demystifying Machine and Deep Learning for Developers by Seth Juarez and Chris Lauren

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.