https://github.com/saranshmanu/s20-interview-assignment
The repository contains the projects completed for the internship rounds as the part of the recruitment process in S20.ai
https://github.com/saranshmanu/s20-interview-assignment
Last synced: about 2 months ago
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The repository contains the projects completed for the internship rounds as the part of the recruitment process in S20.ai
- Host: GitHub
- URL: https://github.com/saranshmanu/s20-interview-assignment
- Owner: saranshmanu
- Created: 2019-09-21T18:41:28.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2019-10-10T19:02:31.000Z (over 5 years ago)
- Last Synced: 2025-02-01T07:19:01.711Z (4 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 22.8 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# S20-Task
The repositiory contains the projects completed for the internship rounds as the part of the recruitment process at an AI based Startup
## Round Projects
- Round 1 - Create a Movie Recommender iOS application using MovieLens dataset in Tensorflow
- Round 2 - Develop a animal classify to detect tiger inside an image
- Round 3 - Prototype to show the concept of Federated Learning and distributed ML training
## What does S20.ai does?
The AI market is dominated by tech giants such as Google, Amazon and Microsoft, offering cloud-based AI solutions and APIs. In the traditional AI methods sensitive user data are sent to the servers where models are trained.
Recently we are seeing the beginning of a decentralized AI model, called Federated Learning, born at the intersection of on-device AI, blockchain, and edge computing/IoT. In contrast to the traditional AI methods, Federated Learning brings the models to the data source or client device for training and inferencing. The local copies of the model on the device eliminate network latencies and costs incurred due to continuously sharing data with the server. Being local, model response is hyper personalized for a particular user. Federated Learning utilizes computing and storage resources on the user’s device reducing cloud infra overheads even at scale. Additionally, Federated Learning techniques are privacy preserved by design.
## Frameworks and Languages
- Keras
- Tensorflow
- Pytorch
- Pysyft
- CoreMLModel
- Swift
- CoreML