How do I track and follow the latest ML Research?

Create a Twitter account and follow other researchers Most ML researchers that are relevant for my field tweet about their latest research papers. By simply following them and checking my twitter account a couple of times a week, I can keep track of their work.

How does arXiv rank papers?

Although arXiv is not peer reviewed, a collection of moderators for each area review the submissions; they may recategorize any that are deemed off-topic, or reject submissions that are not scientific papers, or sometimes for undisclosed reasons.

Which publications websites do you use to keep up to date on deep learning?

Here are a few sites that I enjoy!

• Netflix Tech Blog on Medium.
• Facebook Research Blog.
• Google AI Blog.
• Uber Engineering Blog (Click here if you want machine learning related content only)
• Airbnb Tech Blog on Medium (Click here if you want machine learning related content only)

Where can I get research papers for machine learning?

Every year, 1000s of research papers related to Machine Learning are published in popular publications like NeurIPS, ICML, ICLR, ACL, and MLDS. The criteria are using citation counts from three academic sources: scholar.google.com; academic.microsoft.com; and semanticscholar.org.

How do you keep your data science practices relevant and up to date?

Social media sites are an easy and quick way to stay up to date on the latest trends, said Silge. Whether it’s new skills, job openings, or best practices, many working professionals share their interests on sites like LinkedIn and Twitter, making those sites a great starting point for staying in the loop.

Is arXiv peer reviewed?

However, the arXiv is not peer reviewed in the formal sense. It is moderated, so that junk submissions can be removed, or manuscripts recategorised, but it lacks the additional layer of quality control of traditional peer review.

What is machine learning blogs?

To keep up to date on the latest industry news, machine learning blogs are a valuable, yet sometimes overlooked, resource. They educate. In the world of artificial intelligence, there is an abundance of blogs that offer illuminating perspectives on recent trends, new products, and industry news.

What are some of the best research papers books for machine learning?

10 Best Machine Learning Textbooks that All Data Scientists…

• Deep Learning.
• Artificial Intelligence: A Modern Approach.
• The Elements of Statistical Learning: Data Mining, Inference, and Prediction.
• The Hundred-Page Machine Learning Book.
• Pattern Recognition and Machine Learning.
• Applied Predictive Modeling.

How do you implement a research paper?

Make sure you cover them carefully each time you are about to start working on such a project.

1. 1.1 – Find an open source implementation to avoid coding it.
2. 1.2 – Find simpler ways to achieve your goal.
3. 1.3 – Beware of software patents.
4. 1.4 – Learn more about the field of the paper.
5. 1.5 – Stay motivated.

Where can I find research papers in machine learning?

Three sources stand out, Arxiv, Conferences, and Google Scholar. Arxiv hosts reprints of publications and is publicly accessible. This combined with the ML researcher’s strong interest in sharing and spreading their work openly makes it one of the top places to find papers.

Is arXiv a good place to submit a research paper?

Arxiv has become one of the hot spots of ML papers. However, submitting to Arxiv is very easy, and sometimes even too easy. There is no peer-review process as you would, for example, have it with well-known conferences. You simply need an “endorsement” from someone already registered.

How do you keep track of your ml research?

Most ML researchers that are relevant for my field tweet about their latest research papers. By simply following them and checking my twitter account a couple of times a week, I can keep track of their work. Additionally, they often retweet other new papers from the field they like.

Is it difficult to stay on top of deep learning research?

With hundreds of research papers being published every year and major technological advancements taking place in the deep learning industry, it is very difficult to stay on top of everything. Even though I love my job, I can s e e that it would be difficult to stay competent and relevant if I do not stay up to date and continue learning.