How to build a model from scratch, select a pretrained model, transfer the learning, compile with optimizers, loss, metrics,, fit the model, and make predicitons for generative, enemble, and classification models.
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.
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
A neural network is a type of machine learning approach used for generating data or predicting labels.
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.
There are over 3 billion base pairs (molecular pieces of information) in the human genome, and on average, 3 million, differences between two individual humans. These differences are transcribed into RNA, which are further sliced and diced before being translated into proteins which drive cellular processes through enzymatic interactions and cell-cell signaling. All these differences and complex processes must be unravelled in order to better understand biological organisms and also to improve human healthcare.
