AWS IoT Core
AWS IoT
ThingSpeak
Particle.io
Blynk.io
Salesforce IoT Cloud
AWS Greengrass
Azure IoT Hub
PyTorch
TensorFlow
Keras
Scikit-learn
OpenCV
CUDA
NumPy
Pandas
AWS IoT Core PyTorchBased on our record, PyTorch seems to be a lot more popular than AWS IoT Core. While we know about 106 links to PyTorch, we've tracked only 8 mentions of AWS IoT Core. 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.
MQTT - AWS IoT Core offers a managed MQTT message broker, giving you easy access to your devices. Fun fact, this is what powers the notifications in Serverlesspresso. - Source: dev.to / 7 months ago
AWS IoT: For real-time communication between the server and the frontend application. - Source: dev.to / about 1 year ago
AWS IoT Core is a service that allows you to connect your devices securely to the AWS cloud and with ease. Option for device management, data processing as well as integration with other AWS services is provided. Click here for more on AWS IoT Core. - Source: dev.to / about 1 year ago
From here you can do all sorts of actions. For example, the serverless-coffee project used IOT Core. With IOT Core you can notify the end-user with status updates. And notify the barista that what kind of coffee needs to be created. - Source: dev.to / about 1 year ago
When you need websockets in a project on AWS most likely API Gateway Websockets (I will refer to it as API Gateway from now on) is the first service coming to mind. At some point when looking into options, I ran into IoT Core instead. I thought this was meant only for very specific scenarios involving hardware; however it also supports MQTT over websockets which makes it an amazing choice for web and app. I think... - Source: dev.to / over 1 year ago
TensorFlow, developed by Google, and PyTorch, developed by Facebook, are two of the most popular frameworks for building and training complex machine learning models. TensorFlow is known for its flexibility and robust scalability, making it suitable for both research prototypes and production deployments. PyTorch is praised for its ease of use, simplicity, and dynamic computational graph that allows for more... - Source: dev.to / 27 days ago
*My post explains Dot, Matrix and Element-wise multiplication in PyTorch. - Source: dev.to / 2 months ago
Import torch # we use PyTorch: https://pytorch.org Data = torch.tensor(encode(text), dtype=torch.long) Print(data.shape, data.dtype) Print(data[:1000]) # the 1000 characters we looked at earlier will to the GPT look like this. - Source: dev.to / 3 months ago
AI's Open Embrace Artificial intelligence (AI) and machine learning (ML) are increasingly leveraging open-source frameworks like TensorFlow [https://www.tensorflow.org/] and PyTorch [https://pytorch.org/]. This democratization of AI tools is driving innovation and lowering entry barriers across industries. - Source: dev.to / 2 months ago
Which label applies to a tool sometimes depends on what you do with it. For example, PyTorch or TensorFlow can be called a library, a toolkit, or a machine-learning framework. - Source: dev.to / 2 months ago
AWS IoT - Easily and securely connect devices to the cloud.
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.
ThingSpeak - Open source data platform for the Internet of Things. ThingSpeak Features
Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.
Particle.io - Particle is an IoT platform enabling businesses to build, connect and manage their connected solutions.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.