Glances
htop
Zapier
GNOME System Monitor
Boomi
Windows Task Manager
MacUncle
ChartsWatcher
PyTorch
TensorFlow
Keras
Scikit-learn
OpenCV
CUDA
NumPy
MLKit
The single customer view you have always wanted is here. Glances unifies your apps in a simplified, easy-to-use customer view that provides real-time data from within any app that you are using. In minutes, securely connect your apps and eliminate tab switching, searching, and clicking around to find important information.
Do the hustle without the hassle
Finding customer information within multiple programs is the hassle that ruins your workflow hustle. Glances brings your favorite online apps together, securely showing your customer data in a single view from whatever app you are using.
An integration the way it should be
It’s like iPaaS, but without the pain. Not time consuming, expensive, or untrustworthy. Glances is a new way to do integrations with a true no-code approach; no data syncing or scheduling jobs. See how it takes just minutes to connect your apps and start using a simplified customer view with Glances.
Glances is designed to support any application that provides an industry standard API, including custom applications. Here is a sample of some of the supported applications:
Glances PyTorchNo features have been listed yet.
Based on our record, PyTorch seems to be more popular. It has been mentiond 106 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.
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 / 12 days ago
*My post explains Dot, Matrix and Element-wise multiplication in PyTorch. - Source: dev.to / about 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 / 2 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 / about 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 / about 2 months ago
htop - htop - an interactive process viewer for Unix. This is htop, an interactive process viewer for Unix systems. It is a text-mode application (for console or X terminals) and requires ncurses. Latest release: htop 2.
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.
Zapier - Connect the apps you use everyday to automate your work and be more productive. 1000+ apps and easy integrations - get started in minutes.
Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.
GNOME System Monitor - System Monitor is a tool to manage running processes and monitor system resources.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.