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Twitter
RT : Want to learn more about and ? Join our next : From Zero to Hero - All you need to do serious…
Meetup  keras  rstats  from twitter_favs
yesterday by cdrago
How to Develop Word-Based Neural Language Models in Python with Keras - Machine Learning Mastery
How to Develop Word-Based Neural Language Models in Python with Keras - Added November 08, 2017 at 10:54AM
keras  machine-learning  neural-networks  tutorial 
yesterday by xenocid
Understand the Difference Between Return Sequences and Return States for LSTMs in Keras - Machine Learning Mastery
Understand the Difference Between Return Sequences and Return States for LSTMs in Keras - Added October 24, 2017 at 09:31PM
keras  machine-learning 
yesterday by xenocid
How to Use Small Experiments to Develop a Caption Generation Model in Keras - Machine Learning Mastery
How to Use Small Experiments to Develop a Caption Generation Model in Keras - Added November 25, 2017 at 10:34AM
deep-learning  keras  machine-learning  neural-networks  read2of 
2 days ago by xenocid
FloydHub - Deep Learning Platform - Cloud GPU
Platform-as-a-Service for training and deploying your DL models in the cloud
deeplearning  tensorflow  keras  cloudcomputing  webtools  GPU  machinelearning  AI  training  models 
2 days ago by sachaa
GitHub Predicts Hottest 2018 Open Source Trends
"The hottest project and community results in 2017, then, would logically foretell growth areas and trends for the coming year. This is what emerged:

Cross-platform development: Projects focused on cross-platform web development experienced the largest growth in activity. The most-starred projects related to Angular, React and Electron, all of which attracted significantly more visitors and contributors in 2017. In particular, Angular/angular-cli more than doubled its contributor base over the past year.
Deep learning: The number of visitors to TensorFlow also more than doubled in 2017, while the TensowFlow/models repo’s visitorship more than quintupled. Keras and Mozilla’s DeepSpeech also saw significant increases in contributions and visitors."
analysis  research  top  stories  angular  container  ecosystem  deepspeech  development  docker  education  electron  github  keras 
3 days ago by jonerp
Handwritten digit recognizer on iOS with Keras and Core ML using the MNIST dataset
The goal of this tutorial is to show the full proceeding to create, train a Deep Learning model and to implement it in an iOS app. The use case here is the “Hello World” of Deep Learning, it is the digit recognition using a dataset of handwritten digits, the MNIST dataset. The model is created and trained by using the Keras framework and is then converted into a Core ML model in order to use it in an iOS app.
neuralnetworks  deeplearning  machinelearning  MNIST  helloworld  Keras  Python  TensorFlow  IOS  CoreMLTools  macOS  Xcode 
3 days ago by areich
How to Develop a Character-Based Neural Language Model in Keras - Machine Learning Mastery
How to Develop a Character-Based Neural Language Model in Keras - Added November 06, 2017 at 09:02AM
keras  machine-learning  nlp  python  read2of  tutorial 
4 days ago by xenocid
How to Define an Encoder-Decoder Sequence-to-Sequence Model for Neural Machine Translation in Keras - Machine Learning Mastery
How to Define an Encoder-Decoder Sequence-to-Sequence Model for Neural Machine Translation in Keras - Added October 26, 2017 at 09:58PM
keras  machine-learning  read2of  tutorial 
4 days ago by xenocid
How I Shipped a Neural Network on iOS with CoreML, PyTorch, and React Native
I’ll walk you through every step, from problem all the way to App Store. On the way we’ll take a quick detour into an alternative approach using simple math (fail), through tool building, dataset generation, neural network architecting, and PyTorch training. We’ll endure the treacherous CoreML model converting to finally reach the React Native UI.
tutorial  coreml  iso  pytorch  keras  onyx  neuralnetworks  cnns 
4 days ago by drmeme

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