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Digests » 26
this week's favorite
BERT (Bidirectional Encoder Representations from Transformers) is a recent paper published by researchers at Google AI Language. It has caused a stir in the Machine Learning community by presenting state-of-the-art results in a wide variety of NLP tasks, including Question Answering (SQuAD v1.1), Natural Language Inference (MNLI), and others.
Gradient descent finds a global minimum in training deep neural networks despite the objective function being non-convex. The current paper proves gradient descent achieves zero training loss in polynomial time for a deep over-parameterized neural network with residual connections (ResNet). Our analysis relies on the particular structure of the Gram matrix induced by the neural network architecture.
We present Open Images V4, a dataset of 9.2M images with unified annotations for image classification, object detection and visual relationship detection.
Neural network 3D visualization framework, build interactive and intuitive model in browsers, support pre-trained deep learning models from TensorFlow, Keras, TensorFlow.js.
Hidden Markov Models are powerful tools, commonly used in a wide range of applications from stock price prediction, to gene decoding, to speech recognition.This is a tutorial on Hidden Markov Models that I wrote, and thought to would make publicly available for download since I believe it captures the intuition quite well.