auserj Goto Github PK

seaborn

Statistical data visualization using matplotlib

seaborn_project

search

Python 3 Search API for Google and Wikipedia

second_order_optimization_newtons_method

security-guide-for-developers

Security Guide for Developers (实用性开发人员安全须知)

security-with-go

Security with Go, published by Packt

selenium-framework-design-in-data-driven-testing

Selenium Framework Design in Data-Driven Testing, published by Packt

self-driving-car

The Udacity open source self-driving car project

self-driving-car-demo

Self Driving Car Demo for Fresh Machine Learning #6

self-driving-car-sim

A self-driving car simulator built with Unity

selfcontrol

:skull: Mac app to block your own access to distracting websites etc for a predetermined period of time. It can not be undone by the app or by a restart – you must wait for the timer to run out.

semantic-image-segmentation

Applying the 100 Layer Tiramisu on the Camvid Dataset

semantic-segmentation_multiple-class

Classify multiple objects pixel by pixel with semantic segmantation technique (Trained with Cityscape-Dataset)

semantic-ui

Semantic is a UI component framework based around useful principles from natural language.

senet-tensorflow

Simple Tensorflow implementation of "Squeeze and Excitation Networks" using Cifar10 (ResNeXt, Inception-v4, Inception-resnet-v2)

sentencepiece

Unsupervised text tokenizer for Neural Network-based text generation.

sentiment-analysis-on-finance-data

The application is a cloud service that provides the functionality of performing sentiment analysis on stock market and financial data. The application can be hosted on Google App Engine and makes use of many of the GAE services like Search Service, MemCache, DataStore etc. Given the name of a company, data from various sources like Twitter, Facebook Graph, Google News, Google Finance etc is aggregated. For each source, different models have been pretrained using some prior data. Using different models provided us with a chance to utilize different Machine Learning methodologies based on the type of data from each source. The various techniques that we have built and tested on are :Naive Bayes, Multinomial and Bernoulli text representations, KNN.

seq2seq

A general-purpose encoder-decoder framework for Tensorflow

seq2seq_model_live

sequence-labeler

Neural network sequence labeling model

sequence-to-sequence-learning-of-financial-time-series-in-algorithmic-trading

My bachelor's thesis—analyzing the application of LSTM-based RNNs on financial markets. 🤓

serverless-resources

A curated list of excellent resources on serverless technologies and architectures

serving

A flexible, high-performance serving system for machine learning models

sgan

Stacked Generative Adversarial Networks

sgx-full-orderbook-tick-data-trading-strategy

Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.

shap

A unified approach to explain the output of any machine learning model.

shapeless

Generic programming for Scala

share-material

每次分享活动相关的材料

sharpefolio

Stock portfolio optimizer in Python based on least correlated moving sharpe / sortino ratios.

shiny-examples