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This project is an example demonstrating how to use Python to train two different machine learning models to detect anomalies in an electric motor. The first model relies on the classic machine learning technique of Mahalanobis distance. The second model is an autoencoder neural network created with TensorFlow and Keras. Data was captured using an ESP32 and MSA301 3-axis accelerometer taped to a ceiling fan. Each sample is about 200 samples of all 3 axes captured over the course of 1 second.

Structuring Jupyter Notebooks For Fast and Iterative Machine Learning Experiments


Unlike in the software world, the term "reusable component" can be hard to apply in the modeling world. Experiments are often one-off, and not many codes were reused. If you're a clean code advocate who likes spending time refactoring every single line of code to follow the "Don't Repeat Yourself" (DRY) principle, you could easily spend too much time doing so. However, I'm not suggesting to go to the opposite of the "Don't Repeat Yourself" principle. I've seen very messy and unorganized Jupyter notebook directories.

Transfer Learning in Computer Vision a case Study


The conclusion to the series on computer vision talks about the benefits of transfer learning and how anyone can train networks with reasonable accuracy. Usually, articles and tutorials on the web don't include methods and hacks to improve accuracy. The aim of this article is to help you get the most information from one source. Stick on till the end to build your own classifier. The ImageNet moment was remarkable in computer vision and deep learning, as it created opportunities for people to reuse the knowledge procured through several hours or days of training with high-end GPUs.

Pachyderm 1.10 - S3 Gateway Expansion & Kubeflow Support


Today we're excited to announce that with Pachyderm 1.10, you can now integrate Pachyderm repos with Kubeflow Pipelines. Pachyderm's S3 Gateway feature lets you directly leverage Pachyderm's data lineage capabilities right inside your Kubeflow environment. Along with the rest of 1.10, this feature signals our commitment to integrate more deeply with the vast and thriving community of data science tools. Ever since we started collaborating with Kubeflow back in 2017, we've seen the great potential of the project and we've enjoyed working with them in the community and the customers we share. Platforms like Kubeflow run their own set of Kubernetes pods.

Facial Recognition with Python and the face_recognition library


Welcome to a tutorial for implementing the face recognition package for Python. The purpose of this package is to make facial recognition (identifying a face) fairly simple. Whether it's for security, smart homes, or something else entirely, the area of application for facial recognition is quite large, so let's learn how we can use this technology. To begin, we need to install everything. Installation instruction splits between Windows and Linux for some dependencies, then there is a common part for them.

Docker Jupyter for Machine Learning


Many data science applications require an isolated model training/development environment from your host environment. The lightweight solution for this would be to integrate Jupyter with Docker. The best practice for setting up such a container is using a docker file, which I have written following the best practices in less than 1 minute. I hope this would help anyone engaging in data science applications with docker. The GitHub repo can be found here.

TensorFlow Serving: Deploying Deep Learning Models Just Got Easier! - Analytics Vidhya


So you've built your machine learning or deep learning model. That final stage – the crucial cog in your machine learning or deep learning project – is model deployment. You need to be able to get the model to the end user, right? And yet you'll face a ton of questions about model deployment when you sit for data scientist interviews! What are the different tools for model deployment?

Project in Python - Breast Cancer Classification with Deep Learning - DataFlair


If you want to master Python programming language then you can't skip projects in Python. After publishing 4 advanced python projects, DataFlair today came with another one that is the Breast Cancer Classification project in Python. To crack your next Python Interview, practice these projects thoroughly and if you face any confusion, do comment, DataFlair is always ready to help you. An intensive approach to Machine Learning, Deep Learning is inspired by the workings of the human brain and its biological neural networks. Architectures as deep neural networks, recurrent neural networks, convolutional neural networks, and deep belief networks are made of multiple layers for the data to pass through before finally producing the output.

Anolytics – Data Annotation Service For Machine Learning AI Directory - Global Artificial Intelligence Directory


Anolytics offers a low-cost annotation service for machine learning and artificial intelligence model developments. It is providing the precisely annotated data in the form of text, images and videos using the various annotation techniques while ensuring the accuracy and quality. It is specialized in Image Annotation, Video Annotation and Text Annotation with best accuracy. Anolytics is providing all leading types of data annotation service used as a data training in machine learning and deep learning. It offers Bounding Boxes, Semantic Segmentation, 3D Point Cloud Annotation for fields like healthcare, autonomous driving or drone falying, retail, security surveillance and agriculture.