Jim Michael

Jim Michael

1576054064

Federated learning with TensorFlow Federated

TensorFlow Federated (TFF) is an open-source framework for machine learning and other computations on decentralized data. TFF has been developed to facilitate open research and experimentation with Federated Learning (FL), an approach to machine learning where a shared global model is trained across many participating clients that keep their training data locally. By eliminating the need to collect data at a central location, yet still enabling each participant to benefit from the collective knowledge of everything in the network, FL lets you build intelligent applications that leverage insights from data that might be too costly, sensitive, or impractical to collect.

In this session, we explain the key concepts behind FL and TFF, how to set up a FL experiment and run it in a simulator, what the code looks like and how to extend it, and we briefly discuss options for future deployment to real devices.

#TensorFlow #MachineLearning #Python

What is GEEK

Buddha Community

Federated learning with TensorFlow Federated
Gussie  Hansen

Gussie Hansen

1618393920

Federated Learning through Distance-Based Clustering

Have you ever imagined how your mobile keyboard predicts the next keyword strokes?
The working/prediction of words is given by an ML algorithm deployed on your mobile and is trained on your local data. As it’s training on your data, you might have a question now, isn’t my privacy lost?
The answer is no. These tech companies use Federated Learning to solve the privacy issues as no data is sent to the primary model. Instead, the ML model will be deployed on your device and trained on the data available, and the model parameters are returned instead of the data. Let’s dive into the working of it, and in this article, we propose a new methodology of clustering similar devices to increase the model’s performance.

List of contents:

  1. Introduction
  2. Federated Learning
  3. Our Framework
  4. Clustering
  5. Phases of Training
  6. Results & Comparision
  7. Conclusion

#tensorflow #ai #deep-learning #machine-learning #federated-learning

Mckenzie  Osiki

Mckenzie Osiki

1623139838

Transfer Learning on Images with Tensorflow 2 – Predictive Hacks

In this tutorial, we will provide you an example of how you can build a powerful neural network model to classify images of **cats **and dogs using transfer learning by considering as base model a pre-trained model trained on ImageNet and then we will train additional new layers for our cats and dogs classification model.

The Data

We will work with a sample of 600 images from the Dogs vs Cats dataset, which was used for a 2013 Kaggle competition.

#python #transfer learning #tensorflow #images #transfer learning on images with tensorflow #tensorflow 2

A comprehensive ML Metadata walkthrough for Tensorflow Extended

Why it exists and how it’s used in Beam Pipeline Components

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ML Metadata (MLMD) is a library for recording and retrieving metadata associated with ML developer and data scientist workflows.

TensorFlow Extended (TFX) is an end-to-end platform for deploying production ML pipelines


The current version of ML Metadata by the time this article is being published is **v0.22 **(tfx is also v0.22). The API is mature enough to allow for mainstream usage and deployment on the public cloud. Tensorflow Extended uses this extensively for component — component communication, lineage tracking, and other tasks.

We are going to run a very simple pipeline that is just going to generate statistics and the schema for a sample csv of the famous Chicago Taxi Trips dataset. It’s a small ~10mb file and the pipeline can run locally.

PIPELINE_ROOT = '<your project root>/bucket' # pretend this is a storage bucket in the cloud
	METADATA_STORE = f'{PIPELINE_ROOT}/metadata_store.db'
	STAGING = 'staging'
	TEMP = 'temp'

	PROJECT_ID = ''
	JOB_NAME = ''

	DATASET_PATTERN = 'taxi_dataset.csv'

	BEAM_ARGS = [
	    '--runner=DirectRunner'
	]

	def create_pipeline():
	    no_eval_config = example_gen_pb2.Input(splits=[
	        example_gen_pb2.Input.Split(name='train', pattern=DATASET_PATTERN),
	    ])
	    example_gen = CsvExampleGen(input=external_input(
	        PIPELINE_ROOT), input_config=no_eval_config)
	    statistics_gen = StatisticsGen(examples=example_gen.outputs['examples'])
	    schema_gen = SchemaGen(statistics=statistics_gen.outputs['statistics'])

	    return pipeline.Pipeline(
	        pipeline_name=f'Pipeline {JOB_NAME}',
	        pipeline_root=PIPELINE_ROOT,
	        components=[example_gen, statistics_gen, schema_gen],
	        beam_pipeline_args=BEAM_ARGS,
	        metadata_connection_config=metadata.sqlite_metadata_connection_config(METADATA_STORE)
	    )

	if __name__ == '__main__':
	    BeamDagRunner().run(create_pipeline())
view raw
metadata_local.py hosted with ❤ by GitHub

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Generated Artifact List

Run it once and open up the metadata_store.db file for inspection.

#metadata #deep-learning #tensorflow #tensorflow-extended #machine-learning #deep learning

Jerad  Bailey

Jerad Bailey

1598891580

Google Reveals "What is being Transferred” in Transfer Learning

Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community — What is being transferred in Transfer Learning? They explained various tools and analyses to address the fundamental question.

The ability to transfer the domain knowledge of one machine in which it is trained on to another where the data is usually scarce is one of the desired capabilities for machines. Researchers around the globe have been using transfer learning in various deep learning applications, including object detection, image classification, medical imaging tasks, among others.

#developers corner #learn transfer learning #machine learning #transfer learning #transfer learning methods #transfer learning resources

Uriah  Dietrich

Uriah Dietrich

1616086260

Gaming ML/AI based on Reinforcement Learning

Today you turn on your TV, you listen to the radio, you read a newspaper and, unbelievable… you most probably come across about machine learning and artificial intelligence.
The Machine Learning approach helps humans by taking decisions to solve a problem into automatic flow identifying hidden patterns, and observing multiple state variables impossible to be detected easily by human beings.
As it is for humans, school is the base for knowing and progressing, so the first thing that is involved in ML is training.
Let’s send ML to school and explore how these algorithms create their knowledge. ;)

#reinforcement-learning #tensorflow #machine-learning-ai #federated-learning #ai