Machine Learning

AI learning evolution

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Object detection is a domain that has benefited immensely from the recent developments in deep learning. Recent years have seen people develop many algorithms for object detection, some of which include YOLO, SSD, Mask RCNN and RetinaNet. For the past few months, I've been working on improving object detection at a research lab.
Tom Silver | About Me | Favorite Papers | Blog By Tom Silver A friend of mine who is about to start a career in artificial intelligence research recently asked what I wish I had known when I started two years ago. Below are some lessons I have learned so far.
Experience flexible research and accelerated production with Facebook's ecosystem of open source, state-of-the-art AI developer tools.
Over the last six months, a powerful new neural network playbook has come together for Natural Language Processing. The new approach can be summarised as a simple four-step formula: embed, encode, attend, predict.
With the growing success of neural networks, there is a corresponding need to be able to explain their decisions - including building confidence about how they will behave in the real-world, detecting model bias, and for scientific curiosity. In order to do so, we need to both construct deep abstractions and reify (or instantiate) them in rich interfaces .
import tensorflow as tf import tensorflow_hub as hub with tf.Graph().as_default(): embed = hub.Module("") embeddings = embed(["A long sentence.", "single-word", ""]) with tf.Session() as sess: print(
Sana Labs is an education tech startup founded by Joel Hellermark, 21. It provides an artificial-intelligence platform designed to individualize a student's learning in subjects like language and math. Applying AI to education has so far proved difficult, and Sana Labs hopes its scalable platform will change that.