How can you stay current on natural language processing developments?

The developments in NLP is happening at a very fast pace. It is rapidly evolving with new things coming up almost every other day. In this scenario, its crucial for all the data scientists to keep themselves updated to find better solutions. For this, one has to be constantly reading blogs & news articles. I personally follow Towards Data Science from the medium publication which provides great explanation. Some articles also describe the practical implementation of the process. Next is obviously, following YouTube vlogs; for some people following a video explanation leads to better grasping of information. Then there are other online courses available which comes in a decent price. But why pay when you can have the skills for free!!!

Today, while the AI is evolving and coming up with a surprise every new day, best way to stay current is -keep yourself updated with every recent change and addition in the market. Let it be reading quality blogs or, attending Live workshops/courses(Like deeplearning.ai New courses), or reading documentation(for example, to keep myself updated,o NLP I sometimes read documentation of LLamaIndex/Langchain), this should include everything.

Today, while the AI is evolving and coming up with a surprise every new day, best way to stay current is -keep yourself updated with every new change and addition in the market. Let it be reading quality blogs or attending the Live workshops, this should include everything. And of course, to gain that confidence, don't miss trying new things on your own.

Maybe it is time to think about what NLP is used for, and whether staying current on it is the best use of your time. In the age of LLMs, do you really still need NLP? NLP, the detection of named entities, sentiment, summarization etc, is a tool, but it isn't the goal. The goal is to understand human language and make it actionable. With NLP results, you still need to apply a lot of logic to understand what the author wants and to act on or reply to it. With Large Language Models, isn't that need significantly diminished? You can even ask an LLM to judge if a text is positive or negative. LLMs are great at summarization, too. I see classic NLP tools on the decline and would much rather recommend staying on top of everything related to LLMs.

Reading blogs, newsletters and “research papers” is very important for staying updated about the developments in not only NLP but any field. So subscribe to good newsletters and read blogs at medium or linkedin.

Helps to get visual explanations, interviews with experts, and on-the-go learning. Find formats that suit your preferences and learning style.

Videos and podcasts do a ton of research. It would be a great place to learn everything in detail. There are a ton of food podcasts out there about the recent developments in AI. A few suggestions are Practical AI, Data Skeptic, AI Today.. and so on.

Picture this: imagine learning NLP from the best minds in the field, just by chilling and listening to podcasts or watching talks. That's exactly what video and podcast content offer! You get insights directly from experts, researchers, and practitioners, gaining a deeper understanding of NLP concepts, methods, and even future possibilities.

To stay current on NLP developments, I watch videos and podcasts featuring interviews, lectures, and discussions with experts. The Lex Fridman Podcast explores AI, machine learning, and NLP with leading figures. PyTorch Developer Day showcases innovations and best practices in PyTorch, while NLP Highlights reviews recent papers and trends in NLP, hosted by the Allen Institute for Artificial Intelligence.

A tech podcast is an excellent option, providing valuable insights not only during commutes but also from my personal experience, I recommend pausing and searching for podcasts talks so that grasp new concept as well, and refrain from moving to the next one until you have fully comprehended the concepts relevant to your specific challenges. Official podcasts also contribute significantly to enhancing your understanding

It helps to gain structured knowledge, hands-on practice, and connect with instructors and peers. Choose courses that align with your skill level and goals.

Get hands-on! NLP courses and workshops make you code alongside experts, mastering skills like building pipelines and using transformers. It's learning "NLP speak" by doing, perfect for beginners and pros alike.

In the realm of Natural Language Processing (NLP), staying ahead necessitates more than mere awareness; it demands active engagement with evolving methodologies. Embracing specialized courses and workshops tailored to NLP offers a strategic pathway to mastery whilst simultaneously demonstrating one's desire to continuously learn and nurture their skill set.

Always plug-in and hands-on! Here one can actively explore and implement solutions to contemporary and historical challenges within NLP, evaluate their pros and cons, get a gist of jargons and understand areas where there's still scope of improvement. This is ideal to gain the context and reasons of all the whys and hows like say, why/how we use transformers, RNNs etc to predict the next word in a sentence? or, understand the sentiment of a given text? to overcome the issues we face by relying just on the word embeddings! For Starters, you can go ahead with DeepLearning course by Andrew NG in Coursera, followed by NLP specialization under DeepLearning.AI in Coursera

I stay updated on NLP by taking courses like the Natural Language Processing Specialization on Coursera, the spaCy IRL workshop series, and the Hugging Face Course. They cover NLP basics such as pipelines, embeddings, and models. Coursera focuses on text processing, sentiment analysis, translation, and speech recognition. spaCy workshops cover named entity recognition and text classification. The Hugging Face Course teaches natural language understanding and generation.

Attending AI Meetup communities, to get in touch with industrialists and researchers who work on recent innovations in AI, and Foundation Models. Keeping updated with recent innovations by acquiring knowledge through the research papers. Attending NLP conferences such as EMNLP, NeuIPS, ICLR, etc would definitely helping keeping up with the developments.

Ditch the lone wolf act in AI! Join online communities (forums, talks) to connect with experts, share ideas, and snag hidden opportunities. It's your crew of AI ninjas, keeping you sharp and connected. Think expert tips, brainstorming sessions, and uncovering cutting-edge tools. It's more than just learning, it's finding your tribe and staying ahead of the curve in the ever-evolving AI landscape. So join the adventure (the possibilities are endless!)

Engage in discussions, learn from others' experiences, and network with the NLP community. Participate online or in-person based on your preferences.

Some ways to stay on top of current NLP developments: - Look out for local developer communities like Google Dev circles, etc. that take on latest AI developments as discussion topic. - Join Reddit communities like r/LanguageTechnology, r/ChatGPT, r/learnmachinelearning to learn from the world and engage in discussions on current topics.

Linkedin provides amazing platform to be part of communities and events that can keep you updated you just have search and follow leaders in this industry. You will get notifications for events that you join remotely/virtually. This is very helpful in being well informed about the latest developments.

In my experience, one of the best methods to master NLP techniques is to choose a project and start working on it. For instance, you can start by predicting a user review's sentiment (e.g., positivity or negativity). First, understand the problem and think about it deeply. Analyze the tools used by others (best practice) and try to mimic how others have approached the problem. Then, try to be creative. For instance, if someone has developed a model for distinguishing between positive and negative tweets about a presidential candidate, ask yourself what is missing (e.g., why it thinks a tweet is positive). The next step is to add this feature by mastering new tools. Keep this loop of improvement alive and you'll succeed!

Helps build projects, test new techniques, and apply your knowledge practically. Start small, iterate, and learn from your experiences.

I can't emphasize enough the importance of experimentation and hobby projects. One of the best projects to begin right now is building your own small RAG that runs entirely on local hardware. No need for an OpenAI key or anything like that -- get familiar with small text embedding models like INSTRUCTOR-Large or BGE-small-en-1.5 -- these are small enough that you can run them on your local hardware, and (in the case of BGE models) even fine-tune them in just a few hours on your personal laptop.

While theory lays the groundwork, it’s hands-on experimentation where the real magic unfolds. Building a chatbot reveals that it’s not mere miracle—it’s dedication and innovation. After a few projects, you’ll find your stride. ????

In my experience, staying updated in Natural Language Processing involves reading various NLP-related blogs on platforms like Medium, Substack, or DailyDoseofDS, and actively practicing by implementing the concepts discussed in these blogs on your own system. For example, if you read an article on word embeddings, you should then tackle a similar problem and apply word embedding techniques to comprehend their workings and applications. This approach to learning will not only keep you updated but also enhance your understanding and skills.

To stay current on natural language processing (NLP) developments, it's essential to engage in continuous learning through various channels. This includes following reputable NLP research publications, attending conferences, workshops, and webinars, such as those organized by organizations like ACL, EMNLP, or NeurIPS. Subscribing to newsletters, blogs, and social media accounts of leading researchers and organizations in the field can provide regular updates on new techniques, models, and applications. Participating in online forums and communities like Reddit's r/LanguageTechnology or joining professional networks such as LinkedIn groups dedicated to NLP can also facilitate knowledge sharing and networking with peers.

LLM is actually a subset of NLP, but nowadays LLM dominates NLP world so much that there is no point learning the traditional NLP. NLP traditionally is about lexical analysis (token level), syntax analysis (sentence level) and semantic analysis (understanding the meaning). Traditional NLP is about classifying text, predicting next words, translation and creating chatbots. But LLM is so much better in all the above so no one is doing traditional NLP any more. So to stay current with NLP development, we just need to keep learning LLM, particularly prompt engineering and Retrieval-Augmented Generation.

It is not one way approach, this multi-faceted approach ensures a well-rounded grasp of the field.I recommend diving into research papers and relevant blogs, subscribing to newsletters from reputable sources, joining NLP communities for discussions, and considering online courses to deepen your understanding.NLP is now traditionally for stay update into nlp we have follow up with LLM powered Retrieval-Augmented Generation. application

To stay current on natural language processing (NLP) developments, individuals can engage actively with the NLP community by following conferences like EMNLP, ACL, and NeurIPS, reading research papers on platforms like arXiv and Google Scholar, and participating in online forums and communities such as Reddit's r/LanguageTechnology and Stack Exchange. Additionally, staying updated on social media by following NLP experts and researchers, taking online courses and tutorials from platforms like Coursera and Udacity, experimenting with NLP tools and libraries like NLTK and spaCy, subscribing to NLP newsletters and blogs, and attending local meetups and webinars can provide valuable insights and opportunities for learning and networking.

To stay current on natural language processing (NLP) developments: Follow Research: Regularly read academic papers and journals on platforms like arXiv and Google Scholar. Attend Conferences: Attend NLP conferences like ACL, EMNLP, and NeurIPS to learn about cutting-edge research and network with experts. Online Courses: Enroll in online courses on platforms like Coursera, Udacity, or edX to access up-to-date content taught by industry professionals. Online Communities: Join NLP communities on platforms like Reddit, LinkedIn, or Slack. Follow Thought Leaders: Follow prominent researchers and thought leaders in the NLP field on social media platforms like Twitter to stay informed about recent developments and discussions.