人工智能资料库：第24辑（20170205）

最新推荐文章于 2025-11-26 15:49:07 发布

原创最新推荐文章于 2025-11-26 15:49:07 发布 · 180 阅读

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【博客】How To Code Your Own Personal Assistant Using Python Programming

简介：

In this article, I’m sharing the efforts of a programmer to create his own python-powered personal assistant. Using open source libraries for text-to-speech conversion and speech recognition, he describes a way to create a personal “Jarvis”.

原文链接：https://fossbytes.com/code-personal-assistant-using-python-programming/

2.【博客】Asynchronous n-steps Q-learning

简介：

Q-leaning in the most famous Temporal Difference algorithm. Original Q-learning algorithm tries to determine the state-action value function that minimizes the error below.

原文链接：https://papoudakis.github.io/announcements/qlearning/

3.【代码】YouTube Like Count Predictor

简介：

This a tool for getting youtube video like count prediction.A Random Forest model was used for training on a large dataset of ~3,50,000 videos.Feature engineering,Data cleaning, Data selection and many other techniques were used for this task.

原文链接：https://github.com/ayush1997/YouTube-Like-predictor

4.【教程】Tutorial on Deep Learning

简介：

Lecture 1: Tutorial on Deep Learning I
Lecture 2: Tutorial on Deep Learning II
Lecture 3: Tutorial on Deep Learning III
Lecture 4: Tutorial on Deep Learning IV

Speaker: Ruslan Salakhutdinov, Carnegie Mellon University

原文链接：https://simons.berkeley.edu/talks/tutorial-deep-learning

5.【博客】Food Classification with Deep Learning in Keras / Tensorflow

简介：

As an introductory project for myself, I chose to use a pre-trained image classifier that comes with Keras, and retrain it on a dataset that I find interesting. I’m very much into good food and home cooking, so something along those lines was appetizing.

In the paper, Food-101 – Mining Discriminative Components with Random Forests, they introduce the Food-101 dataset. There are 101 different classes of food, with 1000 labeled images per class available for supervised training.

原文链接：http://blog.stratospark.com/deep-learning-applied-food-classification-deep-learning-keras.html?utm_campaign=Revue%20newsletter&utm_medium=Newsletter&utm_source=revue