Scientific machine learningSensitivity analysisHilbert–Schmidt independance criterionHyperparameter optimizationInterpretabilityTackling new machine learning problems with neural networks always means optimizin
未来Deep learning将会成为生信的标准工具,这是大势所趋,不可阻挡。 我目前在研究的MIRA就是使用了Autoencoder,这个已经在单细胞领域非常成熟了。【清一色NC灌水】 降噪- Single-cell RNA-seq denoising using a deep count autoencoder 空间- Deciphering spatial domains from spatially resolved transcriptomics with ...
DeepLearning---Meta Learning Intruduction06-18 收起 超参数优化 超参数在很大程度上可以决定模型的训练效果,例如学习率影响学习效率,正则化影响泛化能力等。 对超参数的优化也一直是一个受人关注的问题,尤其是可调整的超参数越来越多,手动调参的消耗越来越大,迫切需要一些可以自动化搜索最佳超参数的算法。 本文...
Here’s how to use Autoencoders to detect signals with anomalies in a few lines of… Piero Paialunga August 21, 2024 12 min read 3 AI Use Cases (That Are Not a Chatbot) Machine Learning Feature engineering, structuring unstructured data, and lead scoring ...
In order to improve reproducibility, deep reinforcement learning (RL) has been adopting better scientific practices such as standardized evaluation metrics and reporting. However, the process of hyperparameter optimization still varies widely across papers, which makes it challenging to compare RL algorithm...
Machine learning is an efficient method for analysing and interpreting the increasing amount of astronomical data that are available. In this study, we show a pedagogical approach that should benefit anyone willing to experiment with deep learning techniques in the context of stellar parameter determinat...
Learning rate Decimal value. The learning rate, also known as shrinkage. This is used as a multiplicative factor for the leaves values. Loss String value. The loss function to use in the boosting process. - binary_crossentropy (also known as logistic loss) is used for binary classification...
The world's cleanest AutoML library ✨ - Do hyperparameter tuning with the right pipeline abstractions to write clean deep learning production pipelines. Let your pipeline steps have hyperparameter spaces. Design steps in your pipeline like components. Compatible with Scikit-Learn, TensorFlow, and ...
(基于 coursera 课程中的一个示例:神经网络和深度学习 - DeepLearning.AI)我面临随机权重初始化的问题。假设我尝试调整网络中的层数。我有两个选择: 1.:将随机种子设置为固定值 2.:在不设置种子的情况下多次运行我的实验 两个版本各有利弊。我最大的担忧是,如果我使用随机种子(例如:),tf.random.set_seed(1...
Hyperparameter optimization is a big part of deep learning. The reason is that neural networks are notoriously difficult to configure and there are a lot of parameters that need to be set. On top of that, individual models can be very slow to train. In this post you will discover how ...