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Supervised learning is a traditional method of training machine learning model. During training, every observed data needs to be labeled. What if we had a way to train machine learning models without collecting labels? What if we extract tags from the same data collected? This type of learning algorithm is called self supervised learning. This ...

Recently tried to use HuggingFace 🤗 The transformers library fine tuned the Bert text classification under pytorch, and found many Chinese blog s, mainly for the processing of data. There is no detailed description, and I don't know how to deal with the format of dataset, so I make a record here.Dependent packagepytorchtransformersscikit-learn ...

Propeller tool assembly - PaddleXThe whole process development tool of the propeller integrates all the capabilities required for in-depth learning and development such as the core framework, model library, tools and components of the propeller, and opens up the whole process of in-depth learning and development.PaddleX also provides a concise ...

Chapter 3 word2vec The topic of this chapter is still the distributed representation of words. In the previous chapter, we obtained the distributed representation of words by using the counting based method. In this chapter, we will discuss the alternative method of this method, that is, the reasoning based method. As the name suggests, t ...

Experimental topic Trigger word detection Experimental content In this experiment, we understand how to apply deep learning to speech recognition. We will build speech data sets and implement trigger word detection algorithms (sometimes referred to as keyword detection or wake-up word detection). Trigger word detection is a technology that a ...

7. Loss function loss Loss usually refers to the difference between the predicted value and the real value of the output of the neural network, which is used to: ① Calculate the gap between the actual output and the target ② Provide some basis for us to update the output (back propagation) For example: the specific definitions of each functio ...

2021SC@SDUSC preface This article analyzes torch in yolov5 code_ Utils.py file is mainly some encapsulated tool classes. torch_distributed_zero_frist function @contextmanager def torch_distributed_zero_first(local_rank: int): """ Decorator to make all processes in distributed training wait for each local_master to do something. ...

1. Introduction to algorithm Particle swarm optimization The idea of is derived from the study of bird predation behavior, which simulates the flight and foraging behavior of bird clusters, and makes the group achieve the optimal purpose through collective cooperation. Imagine a scenario where a group of birds are randomly searching for food. ...

I. Introduction Problem: Existing CNN s require fixed-size input pictures, which can be achieved by clipping or distorting the original image. These two methods may have different problems: (i) The clipped area may not contain the whole object; (ii) The distortion operation results in unnecessary geometric distortion of the target. If the targ ...

Original address: pytorch_lightning whole journey notes - Zhihu preface This article will be updated continuously. After my algorithm is trained, I will write another record about the experience of pytorch lightning in reinforcement learning. There's already a lot about pytorch_lightning (pl). In a word, this framework is really fragrant, in ...