Difference between revisions of "Watch me Build a Cybersecurity Startup"
| Line 24: | Line 24: | ||
* [http://github.com/facebook/create-react-app Create React App | GitHub] | * [http://github.com/facebook/create-react-app Create React App | GitHub] | ||
* [[Python#Flask |Flask]] microframework for Python | * [[Python#Flask |Flask]] microframework for Python | ||
| − | * [[AWS Lambda & Python]] | + | * [[Amazon |AWS]] tools: |
| + | ** [[Lambda]] - run code without managing servers | ||
| + | ** [[Kinesis]] - collect, process, and analyze real-time, streaming data | ||
| + | ** [[Simple Storage Service (S3)]] - object storage | ||
| + | ** [http://aws.amazon.com/athena Athena] interactive query service to analyze data in Amazon S3 using standard SQL | ||
| + | ** [[AWS Lambda & Python]] | ||
| + | ** [[AWS with TensorFlow]] | ||
* [[Cybersecurity]] | * [[Cybersecurity]] | ||
Revision as of 11:05, 2 September 2019
YouTube search... ...Google search
- How do I leverage AI?
- Watch me Build a Marketing Startup | Siraj Raval
- Watch me Build a Healthcare Startup | Siraj Raval
- Watch me Build a Finance Startup | Siraj Raval
- Watch me Build a Retail Startup | Siraj Raval
- Watch me Build a Trading Bot | Siraj Raval
- DharmaSecurity Demo
- OpenMined
- Analytics Dashboard React Application
- Node.js - JavaScript runtime built on Chrome's V8 JavaScript engine
- React = JavaScript library for building user interfaces
- Create React App | GitHub
- Flask microframework for Python
- AWS tools:
- Lambda - run code without managing servers
- Kinesis - collect, process, and analyze real-time, streaming data
- Simple Storage Service (S3) - object storage
- Athena interactive query service to analyze data in Amazon S3 using standard SQL
- AWS Lambda & Python
- AWS with TensorFlow
- Cybersecurity
DharmaSecurity
I've built a Demo app called DharmaSecurity, a fraud detection tool for businesses. The way it works is that once signed up, a business will paste a code snippet into their website, and then they'll get access to a dashboard that tells them how many fraudulent accounts they have. Our app will use machine learning to automatically remove suspected fraud accounts, and flag likely ones for review. To build this, I use a suite of AWS tools, Python, Javascript, a Logistic Regression (LR) model, a credit card fraud dataset, and a library called OpenMined to enable federated learning and secure multi-party computation. I've packed a lot into this video, animations, code, music, screencasts, skits, etc. Enjoy! Code for "a Cybersecurity Startup" | Siraj Raval - GitHub
