Learn what it takes to be a solo founder at an ML startup.

Image for post
Image for post

Hi everyone!

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. …


Resources for getting started with natural language processing.

Image for post
Image for post

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. …


Course recommendations for getting started with machine learning

Image for post
Image for post

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.

Elements of AI

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. …


Here is a compilation of books, courses, and repositories to get you started.

Image for post
Image for post

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.

📘 Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition

by Aurélien Géron

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. …


Here is a compilation of books, videos, and papers to get you started.

Image for post
Image for post

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.

Mathematics for Machine Learning

by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong

Source: https://mml-book.github.io

Note: this is probably the place you want to start. Start slowly and work on some examples. …


Advice on getting started with applied ML research

Image for post
Image for post
Photo by Nick Morrison on Unsplash

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. …


The future of GANs and applications.

Image for post
Image for post

Yesterday, I attended the amazing “GANs for Good” panel discussion hosted by deeplearning.ai, and here are my takeaways:

  • Generative adversarial networks (GANs) have been improved over the years and are starting to see adoption in the real world in domains such as health, art, and augmented reality. A conversation on progress and responsible use is needed.
  • Current progress and iterations of GANs show that we have gone from generating simple low-resolution images to high-resolution realistic images. However, applications beyond simple image generation are starting to surface.
  • One interesting application of GANs is the manufacturing of dental crowns which speeds up the whole process for the patient. A procedure that may take weeks could now be done with high precision in hours. …


In this issue, we cover topics that range from the importance of taking NLP beyond English to resources for monitoring ML systems to a conversation on the future of conversational AI systems.

Image for post
Image for post

Hello everyone,

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. …


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.

Image for post
Image for post

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.

Special thanks to Keshaw Singh and Manikandan Sivanesan for significantly contributing towards this edition of the NLP Newsletter.

dair.ai updates

  • In one of our upcoming talks, Dr. Juan M. Banda will discuss the motivation and rationale behind their Social Media Mining Toolkit (SMMT), and how to use it to define frameworks for large-scale social media data gathering for NLP and machine learning research projects. They will outline all the lessons learned, mistakes, and hard decisions made to produce and maintain a publicly available large-scale dataset of COVID-19 Twitter chatter data featuring over 424 Million Tweets in 60+ languages and from 60+ countries. …


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.

Image for post
Image for post

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. …

About

elvis

ML and NLP Research Scientist | Ph.D. | Twitter: (https://twitter.com/omarsar0)

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store