The learning process is governed by an algorithm— a sequence of instructions written by humans that tells the computer how to analyze data — and the output of this process is a statistical model encoding all the discovered patterns. This can then be fed with new data to generate predictions....
building up a powerful representation of language without having to label parts of speech and other grammatical features. Transformers, in fact, can be pretrained at the outset without a particular task in mind. After these powerful representations are learned, the models can later be ...
Multi-class image classificationTasks where an image is classified with only a single label from a set of classes - for example, each image is classified as either an image of a 'cat' or a 'dog' or a 'duck'. Multi-label image classificationTasks where an image could have one or more...
notes:https://laisky.notion.site/What-Is-ChatGPT-Doing-and-Why-Does-It-Work-6d390e2e44eb40498bd8b7add36bcc94?pvs=4slides:https://s3.laisky.com/public/slides/What%20Is%20ChatGPT.slides.html#/Ⅰ、It’s Just Adding One Word at a Time在 GPT 流利的对话背后,GPT 实际上只专注于做一件事...
Basically, human experts create an AI Auto-label model that marks raw, unlabeled data. After that, they identify whether the model has done the labeling correctly. In the case of failure, human labelers correct the errors and re-train the model.Synthetic data development. Synthetic data is ...
Self-supervised learning sees widespread use in computer vision andnatural language processing (NLP)tasks requiring large datasets that are prohibitively expensive and time-consuming to label. Supervised versus reinforcement learning Reinforcement learningtrains autonomous agents, such as robots and self-drivin...
We could assign this image a label such as “an aerial photograph of solar panels” but this misses out on a lot of the information in the image; documenting deeper knowledge for a large dataset is difficult. But, DINOv2 shows that labels are not necessary for many tasks such as classific...
In general, one-hot encoding is preferred, as label encoding can sometimes confuse the machine learning algorithm into thinking that the encoded column is ordered. To use numeric data for machine regression, you usually need to normalize the data. Otherwise, the numbers with larger ranges might ...
The field of “BERTology” aims to locate linguistic representations in large language models (LLMs). These have commonly been interpreted as rep
Deciding between one-hot encoding, label encoding, or other techniques depends on the data and the modeling approach. Feature Engineering Challenges: Creating useful data features can be hard, needing creativity and expertise. Avoiding too many features or ones that don't fit well is tricky. Data...