Ross's classic bestseller, Introduction to Probability Models, has been used extensively by professionals and as the primary text for a first undergraduate course in applied probability. It provides an introduction to elementary probability theory and stochastic processes, and shows how probability theory can be applied to the study of phenomena in fields such as engineering, computer science, management science, the physical and social sciences, and operations research. With the addition of several new sections relating to actuaries, this text is highly recommended by the Society of Actuaries. A new section (3.7) on COMPOUND RANDOM VARIABLES, that can be used to establish a recursive formula for computing probability mass functions for a variety of common compounding distributions. A new section (4.11) on HIDDDEN MARKOV CHAINS, including the forward and backward approaches for computing the joint probability mass function of the signals, as well as the Viterbi algorithm for determining the most likely sequence of states. Simplified Approach for Analyzing Nonhomogeneous Poisson processes Additional results on queues relating to the (a) conditional distribution of the number found by an M/M/1 arrival who spends a time t in the system,; (b) inspection paradox for M/M/1 queues (c) M/G/1 queue with server breakdown Many new examples and exercises.

resnet-18：ResNet-18的回购

TensorFlow中的深度学习模型 该存储库包含使用实现几种深度学习模型的jupyter笔记本。 每个笔记本均包含有关每种型号的详细说明，希望可以简化所有步骤。 笔记本在Python 3.6，Tensorflow 1.8中运行 楷模：

敏锐模型动物园 Acuity模型动物园包含一组由Acuity工具包创建或转换的流行神经网络模型（来自Caffe，Tensorflow，PyTorch，TFLite，DarkNet或ONNX）。 模型查看器 Acuity使用JSON格式描述神经网络模型，并且我们提供了一个来帮助可视化数据流图。 从4.6.8开始，模型查看器是一部分。 分类 （ ） （ ） （ ） （ ） （ ） （ OriginModel ） Mobilenet-v2 （ OriginModel ） Mobilenet-v3 （ OriginModel ） EfficientNet （ OriginModel ） EfficientNet（EdgeTPU） （ OriginModel ） Nasnet-Large （ OriginModel ） Nasnet-Mobile （ Or

英文名：Mit - Probabilistic Graphical Models - Principles and Techniques.pdf， 中文名：概率图模型,概率图论模型 机器学习选用教材

关于计算机视觉的电子书，最新版本，经典著作，适合工学理学等领域

Training language models to follow instructions论文精读 速速了解InstructGPT

链接永久有效，将模型解压放入py-faster-rcnn/data即可

yolov5s model

t学生混合模型 论文的实施：“使用t分布进行鲁棒混合建模”，D。Peel和GJ McLachlan。 与Python 2.7和Python 3兼容。 依存关系 scikit学习v0.18.1 的numpy v1.11.0 scipy v0.19.0 setuptools v36.0.1 安装 使用pip（无需克隆此存储库）： pip install smm --user 手动： git clone https://github.com/luiscarlosgph/t-Student-Mixture-Models.git cd t-Student-Mixture-Models python setup.py build python setup.py install --user 用法 请参阅示例。 python src/smm/example.py 测验 要运行测试，