作者: William H. Press / Brian P. Flannery / Saul A. Teukolsky / William T. Vetterling 本书编写了300多个实用而有效的数值算法C语言程序。其内容包括：线性方程组的求解，逆矩阵和行列式计算，多项式和有理函数的内插与外推，函数的积分和估值，特殊函数的数值计算，随机数的产生，非线性方程求解，傅里叶变换和FFT，谱分析和小波变换，统计描述和数据建模，常微分方程和偏微分方程求解，线性预测和线性预测编码，数字滤波，格雷码和算术码等。全书内容丰富，层次分明，是一本不可多得的有关数值计算的C语言程序大全。本书每章中都论述了有关专题的数学分析、算法的讨论与比较，以及算法实施的技巧，并给出了标准C语言实用程序。这些程序可在不同计算机的C语言编程环境下运行。 本书可作为从事科学计算的科技工作者的工具书，计算机软件开发者的参考书，也可以作为大学本科生和研究生的参考书或教材。

he QNX Neutrino Cookbook: Recipes for Programmers provides “recipes” that will help you understand how to design and write programs that run on the QNX Neutrino RTOS. There's a separate archive of the source code for the programs that the book describes.

数值分析(Numerical Recipes)3rd Edition。原版教材，含源代码。C语言版本。 William H. Press (Author), Saul A. Teukolsky (Author), William T. Vetterling (Author), Brian P. Flannery (Author)

PyTorch越来越流行，但关于它的书籍还不多，这是一本刚刚出炉的新书，对提高很有帮助，拿去吧。PyTorch Recipes A Problem-Solution Approach 2019

《数值分析》(Numerical Recipes)3rd Edition 和 C & C++ 代码

Numerical.Recipes.3rd , C++数值算法 第三版, 英文版 带书签和源代码.rar

Deep learning doesn't have to be intimidating. Until recently, this machine-learning method required years of study, but with frameworks such as Keras and Tensorflow, software engineers without a background in machine learning can quickly enter the field. With the recipes in this cookbook, you'll learn how to solve deep-learning problems for classifying and generating text, images, and music. Each chapter consists of several recipes needed to complete a single project, such as training a music recommending system. Author Douwe Osinga also provides a chapter with half a dozen techniques to help you if you're stuck. Examples are written in Python with code available on GitHub as a set of Python notebooks. You'll learn how to: Create applications that will serve real users Use word embeddings to calculate text similarity Build a movie recommender system based on Wikipedia links Learn how AIs see the world by visualizing their internal state Build a model to suggest emojis for pieces of text Reuse pretrained networks to build an inverse image search service Compare how GANs, autoencoders and LSTMs generate icons Detect music styles and index song collections