A playbook for systematically maximizing the performance of deep learning models.
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A Compiler Writing Journey
This guide details creating a secure Linux production system. OpenSCAP (C2S/CIS, STIG).
How to systematically secure anything: a repository about security engineering
- security-engineering
- security-architecture
- secure-systems
- threat-modeling
Go DDD example application. Complete project to show how to apply DDD, Clean Architecture, and CQRS by practical refactoring.
- go
- golang
- ddd
- clean-architecture
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.
Quantitative analysis, strategies and backtests
- quantitative-finance
- quantitative-trading
- portfolio-management
- risk-management
Drawing Bayesian networks, graphical models, tensors, technical frameworks, and illustrations in LaTeX.
Cookbook for Node on FHIR and the clinical Meteor.js build pipeline. Create FDA, HIPPA, and HL7 compliant Javascript/Node applications!