Keras
TensorFlow
PyTorch
Scikit-learn
TFlearn
OpenCV
Clarifai
Microsoft Cognitive Toolkit (Formerly CNTK)
ThingSpeak
AWS IoT
Azure IoT Hub
Blynk.io
Ubidots
Particle.io
AWS IoT Analytics
Thingsboard
ThingSpeakBased on our record, Keras should be more popular than ThingSpeak. It has been mentiond 31 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
As a beginner, I was looking for something simple and flexible for developing deep learning models and that is when I found Keras. Many AI/ML professionals appreciate Keras for its simplicity and efficiency in prototyping and developing deep learning models, making it a preferred choice, especially for beginners and for projects requiring rapid development. - Source: dev.to / 28 days ago
After setting the variables for the environment, the next step is to install dependencies. To use Gemma, KerasNLP is the dependency used. KerasNLP is a collection of natural language processing (NLP) models implemented in Keras and runnable on JAX, PyTorch, and TensorFlow. - Source: dev.to / about 1 month ago
Other popular machine learning tools include PyTorch, Keras, and Scikit-learn. PyTorch is an open-source machine learning library developed by Facebook that is known for its ease of use and flexibility. Keras is a high-level neural networks API that is written in Python and is known for its simplicity. Scikit-learn is a machine learning library for Python that is used for data analysis and data mining tasks. - Source: dev.to / 11 months ago
I'm not aware of anything off-the-shelf, but if you have sufficient programming experience, one way to do this would be to build a large dataset of reference images and pictures and use something like keras to train a convolutional neural network on them. Source: about 1 year ago
Pandas comes with many complex tabular data operations. And, since it exists in a Python environment, it can be coupled with lots of other powerful libraries, such as Requests (for connecting to other APIs), Matplotlib (for plotting data), Keras (for training machine learning models), and many more. - Source: dev.to / over 1 year ago
First of all, you need to ask yourself how familiar you are with MatLab. Then from a dev point of view, could you use an API to reference cloud data then apply analytics. Great intro to IoT. I can see that company going far in 5-10 and may invest based on trajectory. Https://thingspeak.com. Source: 8 months ago
You can use solutions like thingspeak https://thingspeak.com/. Source: about 1 year ago
I'm not sure yet. Maybe something custom, but probably not. I was thinking about Thingspeak before. Source: about 1 year ago
I haven't got around to MQTT yet, but as an easy interim solution I recommend ThingSpeak https://thingspeak.com/ as you can set up an account for free and getting an ESP to send data to it is trivial. Plus you can access it via the web, or embed their graphs and dials into a webpage. The graphics are a bit meh though. Source: over 1 year ago
ThingSpeak for IoT Projects Data collection in the cloud with advanced data analysis using MATLAB Https://thingspeak.com/. Source: over 1 year ago
TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.
AWS IoT - Easily and securely connect devices to the cloud.
PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...
Azure IoT Hub - Manage billions of IoT devices with Azure IoT Hub, a cloud platform that lets you easily connect, monitor, provision, and configure IoT devices.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Blynk.io - We make internet of things simple
