With the rise of Alexa, Google Home, Siri etc, one thing is certain that NLP powered assistants are going to become an important technology to developers. I will explain what exactly is NLP (Nature Language Processing), its technology and applications. by Aswathi Nambiar, 5 min read. Continue reading “NLP Explained”
This post is brought to you by one of our speakers, Francois Chollet (@fchollet), who best known as “creator of Keras”. and his thoughts on AI either to manipulate users or give control back to users. 10 min read. Continue reading “Speaker Post: What worries me about AI”
we describe some of the technical challenges we face for video streaming at Netflix and how statistical models and machine learning techniques can help overcome these challenges. by Chaitanya Ekanadham, 8 min read. Continue reading “Using Machine Learning to Improve Streaming Quality at Netflix”
We believe attending the conference is just a part of learning and practice AI. learning list recommended by speakers. Continue reading “Challenge to Learn AI by AI NEXTCon”
One-day Free event for developers of all skill level to learn create Actions for Google Assistant. The event features a blend of tech talks, code labs, discussions, demos and networking. Continue reading “Free Hands-on Workshop: Create Actions for Google Assistant – Seattle, Silicon Valley”
You are invited to join at AI NEXTCon on 4/10-13. It’s 4 days conference focusing on AI tech. 60+ deep dive tech talks (Jeff Dean, Jure Leskovec keynotes), one of largest AI tech event specially geared to tech engineers.
we focus on the personalization aspect of model building and explain the modeling principle as well as how to implement Photon-ML so that it can scale to hundreds of millions of users. by linkedin. 8 min read.
What are Markov chains, when to use them and how they work. by Devin Soni. 5 min read. Continue reading “Markov Chains Explained”
Additional commonly used machine learning algorithms explained. by Shashank Gupta. 10 min read. Continue reading “Machine Learning Algorithms Every Data Scientist Should Know – Part 2”