第二章:Supervised Learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); 第三章:Learning Theory (regularization and model selection); 第四章:Unsupervised Learning (clustering, dimensionality reduction, kernel methods); 第五章:Reinforcement ...
Andrew Ng -- Stanford University CS 229 Machine Learning This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); le...
Notes https://www.scmp.com/news/world/europe/article/3149735/air-pollution-kills-7-million-year-says-who-it-tightens, last accessed on 2022-02-15. https://www.dataversity.net/a-brief-history-of-machine-learning/, last accessed on 2022-02-10. https://ourworldindata.org/, last accessed...
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet,...
This process is called feature engineering, where the use of domain knowledge of the data is used to create features that, in turn, help machine learning algorithms to learn better. In Azure Machine Learning, data-scaling and normalization techniques are applied to make feature engineering easier....
My personal notes for learning about programming and math (in particular statistics). So far it's mostly just my solutions to book exercises, maybe I'll write up some things later on. This repo consists of my studying after leaving university, I won't upload the notes from before that. ...
Below contains a high-level summary of my reviews on all of the classes I took, along with a plan for how I would approach learning machine learning if I could start over. Click to expand each course for the full version with notes. In-depth reviews of machine learning courses: ...
The second part illustrates how fundamental supervised and unsupervised learning algorithms can inform trading strategies in the context of an end-to-end workflow.Chapter 6, The Machine Learning Process, sets the stage by outlining how to formulate, train, tune, and evaluate the predictive ...
Versions Notes Abstract This article introduces a systematic review on arousal classification based on electrodermal activity (EDA) and machine learning (ML). From a first set of 284 articles searched for in six scientific databases, fifty-nine were finally selected according to various criteria establ...
Comparison of machine learning algorithms Some algorithms make particular assumptions about the structure of the data or the desired results. If you can find one that fits your needs, it can give you more useful results, more accurate predictions, or faster training times. ...