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CuPy is a part of the NumPy ecosystem array libraries [7] and is widely adopted to utilize GPU with Python, [8] especially in high-performance computing environments such as Summit, [9] Perlmutter, [10] EULER, [11] and ABCI.
Numpy is one of the most popular Python data libraries, and TensorFlow offers integration and compatibility with its data structures. [66] Numpy NDarrays, the library's native datatype, are automatically converted to TensorFlow Tensors in TF operations; the same is also true vice versa. [ 66 ]
Box2D was first released as "Box2D Lite", a demonstration engine to accompany a physics presentation given by Erin Catto at GDC 2006. On September 11, 2007, it was released as open source on SourceForge.
The torch package also simplifies object-oriented programming and serialization by providing various convenience functions which are used throughout its packages. The torch.class(classname, parentclass) function can be used to create object factories ().
It is free and open-source software, and can be implemented with Python Tools for Visual Studio, which is a free and open-source extension for Microsoft's Visual Studio IDE. [ 2 ] [ 3 ] IronPython is written entirely in C# , although some of its code is automatically generated by a code generator written in Python.
The Wing Python IDE is a family of integrated development environments (IDEs) from Wingware created specifically for the Python programming language with support for editing, testing, debugging, inspecting/browsing, and error-checking Python code. There are three versions of the IDE, each one focused on different types of users:
Objects detected with OpenCV's Deep Neural Network module (dnn) by using a YOLOv3 model trained on COCO dataset capable to detect objects of 80 common classes. Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. [1]
We can then implement a deep network with TensorFlow or Keras. Hyperparameters must also be defined as part of the design (they are not learned), governing matters such as how many neurons are in each layer, learning rate, step, stride, depth, receptive field and padding (for CNNs), etc. [167]