Theoretical principals behind the implementation to foster undersanding of the application goals.
A visualization grammar is a set of rules or guidelines that describe how to create visualizations in a consistent and effective way. It defines a common vocabulary and structure for creating and interpreting visualizations, and can be used to create visualizations that are easy to understand and communicate. The grammar can include rules for things like color selection, chart types, and data encoding.
Data visualization tools are software applications that allow users to create and display data in a graphical or pictorial format. They are used to explore, analyze, and communicate data, and to make it more accessible and understandable. Some common types of data visualization tools include:
Bar charts, line charts, and scatter plots: These tools are used to create basic charts and plots that are commonly used to display data trends over time or to compare values across different categories.
Yes, but not easily. In the United States, the patentability is determined by the U.S. Patent and Trademark Office (USPTO). To be patentable, an invention must be novel, non-obvious, and useful. In this case, novelty and non-obviousness can be difficult to demonstrate. This is because many machine learning algorithms are based on existing techniques and can be seen as an obvious extension of prior art.
There’s a lot of work to get the data ready. AI can be powerful for clinical decision support, but requires big data which requires a lot of data management effort. Use the following table to assess and communicate how ready your data is.
1. Don’t use a neural net if there’s another way
See Sklearn machine learning roadmap for ideas
2. Don’t leak data across datasets,
...and also, Validation is not test
3. Plan for data preparation
Use the table in the data readiness article to communicate the state of readiness.
4. Capture a baseline model
Data visualization is essential to data science and machine learning. Visualization aids in understanding the data and improving model performance, which is why visualization is effective in every step of the analytical process. It can help the scientist understand patterns in the data, like trends and outliers, which can drive data transformation and model hyperparameter tuning. Data visualization can also help communicate the data shortcomings and model interpretation to stakeholders.
This article attempts to demystify batch size, answering all your questions, including: "What is batchsize?", "Why is batch size important?", "Can batch size affect behavior?", "Can batch size be too large?, "Can batch size be too small?", and more. Information is presented here in question/answer format.
A neural network is a type of machine learning approach used for generating data or predicting labels.
Confusion matrices are used for reporting performance of a model.
Important terminology to remember:
Simply put, bioinformatics is the study of biology using computers. Computational biology, clinical informatics, and even systems biology all share a similarly in this definition, with nuanced differences defined by the communities of practice using the term(s). Often these terms are used synonymously, and sometimes they are used with great precision within communities of practice. For these reasons, it is necessary to levelset on a definition.
