89 skills found · Page 2 of 3
ajaybhargav / Lwip NatlwIP stack with NAT implementation (ported from RT-Thread)
omertuc / FizzgolfThis repo contains the submissions to the high-throughput fizzbuzz code golf stack exchange thread and some code to build/run and plot their results
QiShare / QiStackFrameLoggerA lightweight thread method call stack printing tool.
Taymindis / Lfstacklock-free LIFO stack by C native built it, easy built cross platform(no extra dependencies needed) , guarantee thread safety memory management ever!
chbo297 / CMainThreadDetector检测主线程卡顿，输出卡顿发生时的堆栈信息. Detect main thread slow ,and output stack symbols.
ARMmbed / Sal Stack NanostackIPv6+6LoWPAN+Thread stack for mbed OS.
Antoinegtir / Discord CloneStart of Discord clone with new Stack (finishing Threads Before)
hayageek / ThreadsafeA Go package providing thread-safe implementations of array, slice, map, stack and queue
robotadam / SocketconsoleA Python module to open a unix socket to dump all threads' stack traces when read
Tans5 / DumpstackMonitor APP ANR and get all threads stack.
yaegashi / MuslstackBinary patch utility to set default thread stack size for musl libc
pzaino / ZvectorAn ANSI C Vector library (Dynamic Array) that is fully configurable, fast, thread safe, reentrant, can store dynamic data structures as well as base datatypes and can be used to create dynamic stacks, dynamic queues and more.
CUSTIS-public / ProcInspWindows process explorer with web UI. Shows list of running processes on multiple (remote) servers. Allows to watch thread stacks of running CLR processes. Allows to watch current executing requests on w3wp workers.
qqzhang / Accumulation Dev公用库 (包括array、list和stack以及typepool数据结构；iocp、epoll等socket模型；thread；timer定时器)，以及示例。 (date:2011.3)
asadaliofficials / PixeldocsA full-stack Google docs clone, including real-time collaboration, comments, threads, an advanced TipTap editor, custom extensions, templates and much more.
reddyprasade / Machine Learning Interview PreparationPrepare to Technical Skills Here are the essential skills that a Machine Learning Engineer needs, as mentioned Read me files. Within each group are topics that you should be familiar with. Study Tip: Copy and paste this list into a document and save to your computer for easy referral. Computer Science Fundamentals and Programming Topics Data structures: Lists, stacks, queues, strings, hash maps, vectors, matrices, classes & objects, trees, graphs, etc. Algorithms: Recursion, searching, sorting, optimization, dynamic programming, etc. Computability and complexity: P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc. Computer architecture: Memory, cache, bandwidth, threads & processes, deadlocks, etc. Probability and Statistics Topics Basic probability: Conditional probability, Bayes rule, likelihood, independence, etc. Probabilistic models: Bayes Nets, Markov Decision Processes, Hidden Markov Models, etc. Statistical measures: Mean, median, mode, variance, population parameters vs. sample statistics etc. Proximity and error metrics: Cosine similarity, mean-squared error, Manhattan and Euclidean distance, log-loss, etc. Distributions and random sampling: Uniform, normal, binomial, Poisson, etc. Analysis methods: ANOVA, hypothesis testing, factor analysis, etc. Data Modeling and Evaluation Topics Data preprocessing: Munging/wrangling, transforming, aggregating, etc. Pattern recognition: Correlations, clusters, trends, outliers & anomalies, etc. Dimensionality reduction: Eigenvectors, Principal Component Analysis, etc. Prediction: Classification, regression, sequence prediction, etc.; suitable error/accuracy metrics. Evaluation: Training-testing split, sequential vs. randomized cross-validation, etc. Applying Machine Learning Algorithms and Libraries Topics Models: Parametric vs. nonparametric, decision tree, nearest neighbor, neural net, support vector machine, ensemble of multiple models, etc. Learning procedure: Linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods; regularization, hyperparameter tuning, etc. Tradeoffs and gotchas: Relative advantages and disadvantages, bias and variance, overfitting and underfitting, vanishing/exploding gradients, missing data, data leakage, etc. Software Engineering and System Design Topics Software interface: Library calls, REST APIs, data collection endpoints, database queries, etc. User interface: Capturing user inputs & application events, displaying results & visualization, etc. Scalability: Map-reduce, distributed processing, etc. Deployment: Cloud hosting, containers & instances, microservices, etc. Move on to the final lesson of this course to find lots of sample practice questions for each topic!
solariun / AtomicxPure C++ non stack displacement that implements cooperative multitask library for SINGLE CORE embedded development on DSPs, Microcontrollers and Processor (ARV, RISCV, ARM(all), TENSY, ESP), while also suitable for applications on Windows, Linux and MacOs and compatible with some RTOSs as well. This library allows full event driven applications while uses SMARTs LOCKS and WAIT/NOTIFY locks to also transport messages, MESSAGE BROKER is also provided (Those uses Message type size_t message and size_t tags, where tag will give meaning to the message). That implementation also introduce thread safe QUEUE (full object) and smart_ptr (to allow better implementation on minimal environment)
PelionIoT / Coap ServiceCoAP service for Thread/6LoWPAN stack.
nicbet / InfozillaThe infoZilla unstructured software engineering data mining tool. It can find and extract source code regions, patches, stack traces, enumerations and itemizations from discussion threads.
LaevusDexter / AsmcgocallThe wrapper over asmcgocall to call C functions synchronously, without extra os threads and overhead on it. Probably won't work since 1.17 release, because of passing function arguments and results using registers instead of the stack.
