Feature learning, Feature EmbeddingAlmost things are related to Dimension Reduction Representation Learning notionDictionary LearningContrastive LearningMultimodal Embeding Embeddings are underratedMachine learning (ML) has the potential to advance the state of the art in technical writing. No, I’m not talking about text generation models like Claude, Gemini, LLaMa, GPT, etc. The ML technology that might end up having the biggest impact on technical writing is embeddings.https://technicalwriting.dev/data/embeddings.htmlFeature learningIn machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task.https://en.wikipedia.org/wiki/Feature_learningPapers with Code - Representation Learning**Representation Learning** is a process in machine learning where algorithms extract meaningful patterns from raw data to create representations that are easier to understand and process. These representations can be designed for interpretability, reveal hidden features, or be used for transfer learning. They are valuable across many fundamental machine learning tasks like [image classification](/task/image-classification) and [retrieval](/task/image-retrieval). Deep neural networks can be considered representation learning models that typically encode information which is projected into a different subspace. These representations are then usually passed on to a linear classifier to, for instance, train a classifier. Representation learning can be divided into: - **Supervised representation learning**: learning representations on task A using annotated data and used to solve task B - **Unsupervised representation learning**: learning representations on a task in an unsupervised way (label-free data). These are then used to address downstream tasks and reducing the need for annotated data when learning news tasks. Powerful models like [GPT](/method/gpt) and [BERT](/method/bert) leverage unsupervised representation learning to tackle language tasks. More recently, [self-supervised learning (SSL)](/task/self-supervised-learning) is one of the main drivers behind unsupervised representation learning in fields like computer vision and NLP. Here are some additional readings to go deeper on the task: - [Representation Learning: A Review and New Perspectives](/paper/representation-learning-a-review-and-new) - Bengio et al. (2012) - [A Few Words on Representation Learning](https://sthalles.github.io/a-few-words-on-representation-learning/) - Thalles Silva <span style="color:grey; opacity: 0.6">( Image credit: [Visualizing and Understanding Convolutional Networks](https://arxiv.org/pdf/1311.2901.pdf) )</span>https://paperswithcode.com/task/representation-learning