主要用途，理解数学公式的同时，通过这几类可视化工具，可以加深对机器学习、深度学习、概率分布知识的理解和应用。
📌Four interactive tools to learn machine learning concepts.
Machine learning is needed to understand and apply in our everyday job as data scientists. Here are interactive tools to learn them:
1. 经典的各类机器学习模型 What-If Tool ✨
What-If Tool is a web-based, notebook-based visualization tool to understand how machine learning behavior works.
What-If Tool was developed to understand the intricacies behind our trained model and experiment with the hypothetical situation.
https://pair-code.github.io/what-if-tool/
2. 数据分布可选择，模型参数可调节的神经网络。Deep Playground ✨
The Deep Playground project is an interactive web-based neural network for people to learn from.
The web is simple enough for any beginner to understand how the neural network works.
http://playground.tensorflow.org
3. 可参数调节几类著名的概率分布：
Probability Distribution by Simon-Ward Jones ✨
Sometimes it is easier to understand probability distribution with a clear visualization. I recommend the Probability Distribution post by Simon-Ward Jones to help you learn.
https://www.simonwardjones.co.uk/posts/probability_distributions/
4. Embedding Projector ✨
The Embedding Projector from TensorFlow provides an interactive visualization to help us understand the embedding layers.
https://projector.tensorflow.org
原创文章，作者：门童靖博士，如若转载，请注明出处：https://www.agent-universe.cn/2022/11/12679.html
