Great Email Newsletters on AI/ML
All newsletters are released weekly.
- Import AI – AI newsletter that summarizes recent news articles and research; I enjoy how honest and succinct this newsletter is; also like that the implications of new algorithms are always discussed by Jack, who is an advocate for improved ML model explicability and data privacy.
- AI News Weekly – curated list of AI-related articles; presents a brief summary of each article describing the latest news, applied use cases and ethics; has ads
- AI Applied use Cases Top News – curated list of the latest AI news articles; subscribers can customize their newsletters according to topics of interest; weekly
- The Batch – newsletter from deeplearning.ai more academic with discussion of recent AI research papers; great breakdown of topics e.g. what’s new, why it matters and the implications
- TOPBOTS – list of various AI articles recently released on TOPBOT.com; articles cover a wide array of topics e.g. NLP, ML in marketing and interpretability; articles tend to be more technical
- PyImageSearch – articles on using Python to tackle real-world computer vision and deep learning problems; very practical, well-done computer vision blog. I highly recommend Adrian’s free email courses.
- ODSC – newsletter with articles spanning various topics geared to beginners, experts and everyone in between. They also offer webinars (free and paid) as well as online meetups.
Podcasts
- Data Skeptic has short episodes that cover various ML concepts
- TWIML AI – Formally This Week in ML & AI is hosted by Sam Charrington who asks really strong questions to the latest and greatest AI/ML researchers.
- Practical AI – aims to make AI accessible to everyone
Find Practical Information on AI/ML
I really enjoy practical resources on AI/ML topics, like tutorials or tips, that save me time on development and deployment of the models. There are many resources on a variety of topics related to Data Science such as ML theory or the ethics of using AI/ML, but it can be hard to consistently find practical experiences of data scientists as they puzzle through real-life modeling challenges. I love learning from others’ experiences and their mistakes so I can accelerate my own learning. Below are some practical tips for learning more efficiently:
- Reddit – You can follow the data science, python, r or machine learning subreddits to get a steady flow of interesting tidbits that you may not have otherwise encountered. You can also search for any specific topics that you are interested in. Think of reddit as one way of crowdsourcing your search for more information.
- Github search – Sometimes when I don’t know if someone has tackled a problem that I have, I search Github for repos. This is a nifty trick that has saved me a lot of time. Why write code from scratch when it’s already done and is freely available? A 5-minute search can potentially save you hours of work.
- Google search for blog articles – When I want to come up to speed on a topic fast, I Google it first and read the top few articles to get a feel. This is typically more than enough to develop an understanding and implement some code off the bat. I really enjoy reading blog articles from personal websites that tell a great story.
- Medium.com – Sign up for their newsletter so that they can recommend interesting topics in AI/ML. Article recommendations are made based on your latest searches.
- Follow various bloggers and the latest AI/ML developments from major tech companies like Google, Uber, or Facebook using an RSS feed reader such as Feedly. It aggregates all of your news sources into one place.
Be Social
Here are some ways of joining and engaging with the ever-expanding data science community:
- Talk to people at Meetups in your city. Some meetups that are awesome include Women in Data, Pythonistas, and Data Science [insert city e.g. London or NYC] .
- Network with folks at conferences online and in person (eventually). You can attend webinars on practically anything related to AI/ML. DockerCon and PyCon are two free conferences that you can check out now as all videos from this and previous years are freely available.
- Maker spaces and co-work spaces for coding and innovation around tech are awesome places to work (when it is safe to do so). Lots of meetups hold events at these spaces. Find the one in your town or city!
- Slack is a great way to interact and learn from folks. Check out this list for AI/ML channels to follow. You can also follow very niche AI/ML topics based on your expertise level, if you are interested in upskilling on a specific topic like audio ML, Apache Spark ML or Spark NLP.
- Network with folks at work. Join AI research paper reading groups or AI forums where folks regularly present their work. Get your name out there – present your work too!
- Eventbrite – Search for local and online events related to AI/ML. I have seen some good, interesting ones. When you subscribe, new AI/ML events are sent to your inbox weekly.
- Twitter – Follow influential Twitter accounts in the data science space.
- LinkedIn – Stay up to date on the latest trends in Data Science by following LinkedIn’s Top Voices 2020 in Data Science & AI.
- YouTube – You can learn a lot very quickly from a well-done video compared to reading. Here are 5 ML channels that you should consider subscribing to. Several channels offer short 5 minute videos – great for a coffee break from coding!
These are the many ways that I stay abreast of the latest AI/ML trends, news and tutorials. Let me know which ways you gain the most value from!
Staying Up-To-Date on AI/ML