arrow_back Deep learning with limited data
Named Entity Recognition using DL methods arrow_forward
Keras: Deep Learning for Python
Submitted by Fariz Rahman (@farizrahman4u) on Friday, 16 June 2017
Section: Full talk Technical level: Advanced
An introduction to the python deep learning library Keras, the philosophy behind Keras, building and training trivial as well as complex models such as GANs, RL, etc using Keras, deploying a Keras model in a production environment, and the future of Keras. Intended audience: Basic algebraic skills and python experience.
- Introduction to DL and DL libraries - Theano, Torch, Tensorflow, Keras
- Keras and the democratization of DL - the philosophy of Keras, importance of API simplicity, Keras as a spec rather than a project
- Building and training basic Keras models - working with datasets, optimizing input pipeline, using pre-trained models
- Advanced models : GANs, Seq2seq, RL.
- Deployment : Deploying a keras model on servers and mobile apps.
- Future of Keras: Roadmap, community, …
- Top Keras contributor. (contributions)
- Keras community member. Respnosibilities include maintaining and contributing to the core Keras repo and Keras community contributions repo, responding to user queries and issues, helping with roadmaps, and suggest new directions for the project.
- Have written various popular libraries that are part of the Keras ecosystem: qlearning4k for RL, RecurrentShop for RNNs, Seq2seq and a couple of others
- Working as Machine Learning Engineer at (datalog.ai)(www.datalog.ai)
- Currently authoring a book on deep learning using Keras with Packt Publishing