It’s been a while! We are back with the regular release cycle of the NLP Newsletter. In this special edition of the NLP Newsletter, I am sharing my conversation with the founder of Booste, Erik Dunteman, on his experience with MLOps and providing pre-trained NLP models via API.
In the next issue, we go back to the normal sharing of machine learning and NLP resources, educational resources, projects, research papers, and tools. Hope you find this issue useful! Please leave some feedback sharing how you think we can improve. Enjoy!
Over the past few years, software engineering interviews have matured and follow more standardized procedures that help applicants prepare better for the interview process. However, the same cannot be said about machine learning interviews. This is understandable especially as the field is still experiencing growth and maturity in all aspects that range from standardized research, industry best practices, and including the standardization of the machine learning interview process. …
I have been studying natural language processing (NLP) since 2013, back when manual feature engineering was very popular in the world of machine learning. We have come a long way since then. I actually specialized in information retrieval and machine learning techniques for my Ph.D., particularly how they apply to social computing and computational linguistics, while at the same time developing approaches for efficient information extraction from large scale text-based data. I am fortunate to have experience with classical machine learning applied to NLP and witnessed firsthand the explosion of deep learning in the field.
Lots of students have been asking me to prepare a guide for how to get started with natural language processing. This blog post is a shot at helping out others based on research, exposure to the field, and personal experience. Although it is not a direct guide, the resources I share here can help you create your own NLP learning path based on your needs. This will be a combination of educational resources that I have come across over the years. I will share my experience in studying these resources and where they are applicable. …
Before you jump into deep learning, I would strongly advise you to do a few introductory machine learning courses to get up to speed with fundamental concepts like clustering, regression, evaluation metrics, etc.
Here is a thread including a few recent courses you can explore:
This is a crosspost of a Twitter thread I published earlier this week.
by University of Helsinki
Note: I have taken many machine learning courses online. I do some courses for fun but always learn something new. “Elements of AI” provides one of the most approachable, free, and fun AI courses I have taken. …
For the last couple of months, I have been doing some research on the topic of machine learning (ML) in production. I have shared a few resources about the topic on Twitter, ranging from courses to books.
In terms of the ML in production, I have found some of the best content in books, repositories, and a few courses. Here are my recommendations for learning machine learning in production.
This is not an exhaustive list but I have carefully curated it based on my research, experience, and observations.
This is one of the most popular machine learning books and with good reason. If you are just getting started with machine learning I suggest you go through this book and explore the examples. The book doesn’t heavily focus on how to deploy ML models (although there is a nice chapter about it towards the end), but it provides a solid foundation on concepts related to machine learning and deep learning including decision trees, SVMs, CNNs, and much more. …
I have always emphasized on the importance of mathematics in machine learning. Here is a compilation of resources (books, videos, and papers) to get you going.
This is not an exhaustive list but I have carefully curated it based on my experience and observations.
This is a repost of my Twitter thread that you can find here. I will keep updating the list here as I come across more useful resources.
by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong
Note: this is probably the place you want to start. Start slowly and work on some examples. …
So you are interested in applied machine learning (ML) research? Oftentimes, a lot of young aspiring machine learning researchers jump straight into reading papers and either get discouraged with the amount of work published on a particular topic or get too caught up reading a lot of papers with very little progress on generating new and exciting research ideas. To avoid these situations and ensuring a healthy start on your research journey, here are some of my tips on how to get started with applied ML research. …
Yesterday, I attended the amazing “GANs for Good” panel discussion hosted by deeplearning.ai, and here are my takeaways:
Welcome to the 14th issue of the NLP Newsletter. First of all, thank you for taking the time to read the newsletter. A few things are changing in the newsletter moving forward and this is for the better. We will be focusing on a few important machine learning and NLP themes centered around three pillars which I believe to be important for our community: education, research, and technologies. In fact, these are the same pillars that we at dair.ai are focusing on and building our initiatives and projects around. …
Hello everyone! Welcome to the 13th issue of the NLP Newsletter. In this issue, we cover topics that range from interesting works presented at the ACL conference to tools for improving the exploration of papers and code to several useful NLP tool recommendations.
Hello everyone! Welcome to the 12th issue of the NLP Newsletter. In this issue, we cover topics that range from progress in language modeling to Transformer-based object detection to how to stay informed with ML.
It has been a month or so since we last published an issue of the NLP Newsletter. The hiatus is over and we are happy to bring back more of the interesting and creative works that have been coming out of the machine learning and natural language processing communities in the past few weeks.
We have taken the time to think about how to improve the newsletter. We have received excellent feedback and we thank you for all the support. …