I want to pursue machine learning as a career but not sure if I am qualified. How can I test myself?


answer Andrew Ng :

You are qualified for a career in machine learning! Whatever your current level of knowledge, so long as you keep working hard and keep learning, you can become expert in machine learning and have a great career there.

To anyone interested in the field, start with learning to program. When you’ve mastered the basics of programming, consider the Machine Learning course (Machine Learning | Coursera), then the Deep Learning specialization (deeplearning.ai).

To go even further, read research papers (follow ML leaders on twitter to see what papers they’re excited about). Even better, try to replicate the results in the research papers. Trying to replicating others’ results is a one of the most effective but under-appreciated ways to get good at AI. Also consider activities like online ML competitions, academic conferences, and keep reading books/blogs/papers.

It’s really not a matter of whether you’re qualified do work in machine learning—I’m sure you are! It’s just a matter of getting the learnings to make yourself more and more qualified.

new paper from Andrew Ng

👇👇👇👇

Postdoc Artificial intelligence for ultrasound imaging at Eindhoven University of Technology, Netherlands

Offer Requirements
SPECIFIC REQUIREMENTS
We are looking for candidates who have:
A PhD degree in a relevant area.
Proven experience both in deep learning and in medical imaging.
The ability to contribute to cross-disciplinary collaborations.
Excellent written and oral communication skills in English.
A result-driven and proactive attitude.
The position is open and can be filled at any time, but no later than 1 January 2020.

https://www.marktechpost.com/job/postdoc-artificial-intelligence-for-ultrasound-imaging-at-eindhoven-university-of-technology-netherlands/

OCT Fingerprints: Resilience to Presentation Attacks. arxiv.org/abs/1908.00102

Simultaneous Iris and Periocular Region Detection Using Coarse Annotations. arxiv.org/abs/1908.00069

Deep Non-Rigid Structure from Motion. arxiv.org/abs/1908.00052

Image Captioning with Unseen Objects. arxiv.org/abs/1908.00047

NeurIPS | 2019

Thirty-third Conference on Neural Information Processing Systems - Accepted Competitions :https://neurips.cc/Conferences/2019/CallForCompetitions

#DeepLearning #NeurIPS #NeurIPS2019

This tool can now change your photo to anime character. I’ll surely try it on my photo soon and post it here 😊
Code: https://github.com/taki0112/UGATIT
Paper: https://arxiv.org/pdf/1907.10830.pdf

Google Scholar reveals its most influential papers for 2019


https://www.natureindex.com/news-blog/google-scholar-reveals-most-influential-papers-research-citations-twenty-nineteen

Python at Netflix
https://medium.com/netflix-techblog/python-at-netflix-bba45dae649e

Personalized, Health-Aware Recipe Recommendation: An Ensemble Topic Modeling Based Approach. arxiv.org/abs/1908.00148

NVIDIA DRIVE Labs:
"Predicting the Future with RNNs.
Learn how recurrent neural networks (RNNs) can help self-driving cars predict the future motion of surrounding traffic."
Read more: https://nvda.ws/2WmipfF

Lightning defers training and validation loop logic to you. It guarantees correct, modern best practices for the core training logic.
Rapid research framework for Pytorch. The researcher's version of keras
https://github.com/williamFalcon/pytorch-lightning

A small team of researchers at Indiana University has created the first global map of labor flow in collaboration with the world's largest professional social network, LinkedIn. The work is reported in the journal Nature Communications.
The study's lead authors are Jaehyuk Park and Ian Wood, Ph.D. students working with Yong Yeol "Y.Y." Ahn, a professor at the IU School of Informatics, Computing and Engineering in Bloomington.
According to the researchers, the study's result represents a powerful tool for understanding the flow of people between industries and regions in the U.S. and beyond. It could also help policymakers better understand how to address critical skill gaps in the labor market or connect workers with new opportunities in nearby communities.
The study showed some unexpected connections between economic sectors, such as the strong ties between credit card and airline industries. It also identified growing industries during the study period from 2010 to 2014, including the pharmaceutical and oil and gas industries—with in-demand skills such as team management and project management—as well as declining industries, such as retail and telecommunications.
IU researchers created the map using LinkedIn's data on 500 million people between 1990 and 2015, including about 130 million job transitions between more than 4 million companies. The researchers gained access to this rare data as one of only 11 teams selected to participate in the inaugural LinkedIn Economic Graph Research program in 2015. They later became one of only two teams—IU and MIT—selected to continue their work beyond 2017. The team worked closely with LinkedIn engineers, including Michael Conover, a graduate of the IU School of Informatics, Computing and Engineering and a senior data scientist at LinkedIn at the time of the study.
In a blog post on LinkedIn, Park compares the study to a "roadmap" to the future economy since the first step in any journey requires understanding the current landscape.
"We expect this study will provide a powerful foundation for further systematic analysis of geo-industrial clusters in the context of business strategy, urban economics, regional economics and international development fields—as well as providing useful insights for policymakers and business leaders," he said.
https://phys.org/news/2019-08-global-economy-collaboration-linkedin.html

"Anyone Can Learn Artificial Intelligence With This Blog"

A Simple, Illustrated Explanation in Colab, By David Code : https://colab.research.google.com/drive/1VdwQq8JJsonfT4SV0pfXKZ1vsoNvvxcH

#artificialintelligence #deeplearning #neuralnetworks

FairSight: Visual Analytics for Fairness in Decision Making. arxiv.org/abs/1908.00176

Curiosity-driven Reinforcement Learning for Diverse Visual Paragraph Generation. arxiv.org/abs/1908.00169

Supervised Learning of the Global Risk Network Activation from Media Event Reports. arxiv.org/abs/1908.00164