Loading repository data…
seanfw / repository
A group of Jupyter notebooks explaining some useful math concepts with examples from neuroscience
A transparent discovery signal based on current public GitHub metadata.
This score does not audit code, security, maintainers, documentation quality, or suitability. Verify the repository and its current documentation before adoption.
A group of Jupyter notebooks explaining some useful math concepts with examples from neuroscience
Selected from shared topics, language and repository description—not editorial ratings.
alibaba /
Project vlayout is a powerfull LayoutManager extension for RecyclerView, it provides a group of layouts for RecyclerView. Make it able to handle a complicate situation when grid, list and other layouts in the same recyclerview.
40/100 healthjuliocesarfort /
A list of public penetration test reports published by several consulting firms and academic security groups.
79/100 healthfilamentgroup /
A group of plugins for responsive tables.
google /
Common Lisp Koans is a language learning exercise in the same vein as the ruby koans, python koans and others. It is a port of the prior koans with some modifications to highlight lisp-specific features. Structured as ordered groups of broken unit tests, the project guides the learner progressively through many Common Lisp language features.
31/100 healthLeanerCloud /
Saves up to 90% of AWS EC2 costs by automating the use of spot instances on existing AutoScaling groups. Installs in minutes using CloudFormation or Terraform. Convenient to deploy at scale using StackSets. Uses tagging to avoid launch configuration changes. Automated spot termination handling. Reliable fallback to on-demand instances.
89/100 health666DZY666 /
micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、regular and group convolutional channel pruning; 3、 group convolution structure; 4、batch-normalization fuse for quantization. deploy: tensorrt, fp32/fp16/int8(ptq-calibration)、op-adapt(upsample)、dynamic_shape
86/100 health