I find myself coming back to the same few pictures when explaining basic machine learning concepts. Below is a list I find most illuminating. 1. Test and training error:Why lower training error is not always a good thing:ESLFigure 2.11. Test and training error as a function of model compl...
《Brief History of Machine Learning》介绍:这是一篇介绍机器学习历史的文章,介绍很全面,从感知机、神经网络、决策树、SVM、Adaboost到随机森林、Deep Learning. 《Deep Learning in Neural Networks: An Overv…
Researchers from the McKelvey School of Engineering at Washington University in St. Louis have developed a machine learning algorithm that can create a continuous 3D model of cells from a partial set of 2D images that were taken using the same standard microscopy tools found in many labs today....
Machine Learning is the practice of using algorithms to parse data, learn insights from that data, and then make a determination or prediction. The machine is ‘trained’ using huge amounts of data. An important feature of machine learning is the ability to scour data at speed and scale far...
Teachable Machine is an easy, but powerful tool to create machine learning models. It allows for easy data capture to create training data sets and uses state of the art algorithms to train machine learning models right in your browser. It is done in a very intuitive web interface. You can...
Interactive Tools for Machine Learning, Deep Learning and Math - Machine-Learning-Tokyo/Interactive_Tools
Deniz Yuret's Homepage I find myself coming back to the same few pictures when explaining basic machine learning concepts. Below is a list I find most illuminating. 1. Test and training error:Why lower training error is not always a good thing:ESLFigure 2.11. Test and training error as a...
230131 Grounding Language Models to Images for Multimodal Generation #multimodal_generation #vision-language 230131 Large Language Models Can Be Easily Distracted by Irrelevant Context #in_context_learning 230206 Chain of Hindsight Aligns Language Models with Feedback #alignment 230209 Toolformer 230211 Char...
Machine learning technologies have been extensively applied in high-performance information-processing fields. However, the computation rate of existing hardware is severely circumscribed by conventional Von Neumann architecture. Photonic approaches have
《Machine learning in 10 pictures》 介绍:Deniz Yuret用10张漂亮的图来解释机器学习重要概念:1. Bias/Variance Tradeoff 2. Overfitting 3. Bayesian / Occam's razor 4. Feature combination 5. Irrelevant feature 6. Basis function 7. Discriminative / Generative 8. Loss function 9. Least squares 10. ...