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Applications:Debugging machine learning models. Code Sample: importeli5eli5.show_weights(model) 16. PyBrain Website:n/a GitHub stars:2,8k Contributors:33 Description:Although inactive, PyBrain offers a range of machine-learning algorithms. It was designed for both beginners and advanced users. Applic...
MOOC : machine learning 和Data Analyst Nanodegree 这里是一些Blog. 机器学习理论 The Elements of statistical Learning Introduction to Statistical Learning 书: Introduction to machine learning A Course in Machine Learning. 还有一些 Watch 15 hours theory of machine learning! 越看越懒得翻,着实没什么营养...
Python Deep Learning: PyTorch vs Tensorflow PyTorch vs Tensorflow: Which one should you use? Learn about these two popular deep learning libraries and how to choose the best one for your project. Algorithms in Machine Learning Dive headfirst into specific Machine Learning algorithms. Get hands-on...
This library is especially suited for supervised learning, and not very suited to unsupervised learning applications like Deep Learning. #6 Seaborn Seaborn is a library for making statistical graphs in Python. It is built on top of matplotlib and also integrated with pandas data structures. Advantag...
At the core of this revolution lies the tools and the methods that are driving it, from processing the massive piles of data generated each day to learning from and taking useful action. Deep neural networks, along with advancements in classical machine learning and scalable general-purpose ...
If you have been to my other repositories likequant tradingorgraph theory, you must have seen me bashing reckless applications of machine learning. Stop selling AI snake oil! Don't get me wrong. I ain't no machine-learning-sceptic. I see great potential in machine learning but I am merely...
An Introduction to Machine Learning Theory and Its Applications: A Visual Tutorial with Examples (toptal.com) A Gentle Guide to Machine Learning (monkeylearn.com) Which machine learning algorithm should I use? (sas.com) 激活和损失函数 Sigmoid neurons (neuralnetworksanddeeplearning.com) ...
Majorly, ML tasks can be categorized as concept learning, clustering, predictive modeling, etc. The ultimate goal of ML algorithms is to be able to take decisions without any human intervention correctly. Predicting the stocks or weather are a couple of applications of machine learning algorithms....
Random forestshave gainedhuge popularity inapplications of machine learning during the last decade due to their good classification performance, scalability, and ease of use. Intuitively, a random forest can be considered as anensembleof decision trees. The idea behind a random forest is to average...