deep-diamond

Let me chip in with some self-promotion. This book explains and executes every single line of code interactively, from low level operations to high-level networks that do everything automatically. The code is built on the state of the art performance operations of oneDNN (Intel, CPU) and cuDNN (CUDA, GPU). Very concise readable and understandable by humans. https://aiprobook.com/deep-learning-for-programmers/ Here's the open source library built throughout the book: https://github.com/uncomplicate/deep-diamond Some chapters from the beginning of the book are available on my blog, as a tutorial series: https://dragan.rocks

If I may drop in with a bit of shameless self-promotion. My "Deep Learning for Programmers: A Tutorial with CUDA, OpenCL, DNNL, Java, and Clojure" book explains and executes every single line of code interactively, from low level operations to high-level networks that do everything automatically. The code is built on the state of the art performance operations of oneDNN (Intel, CPU) and cuDNN (CUDA, GPU). Very concise readable and understandable by humans. https://aiprobook.com/deep-learning-for-programmers/ Here's the open source library built throughout the book: https://github.com/uncomplicate/deep-diamond Some chapters from the beginning of the book are available on my blog, as a tutorial series: https://dragan.rocks

Does 'convo' refer to a 2D convolution or a 1D convolution? Given the large number [1] of arguments that a convolution can take, which ones are being specified? I can probably guess, since only 2 are given, but if there were more, which order would they be in and which would refer to which? The code is on github [2, 3] see for yourself if you think it's more or less obvious than the python equivalent of a 'trivial' network. I would say 'conv2d' is probably reasonably standard in meaning; I refer to 'convo' as arbitrary, because it is. Either (ideally) avoid abbreviations, or use standard ones. [1] - https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.ht... [1] - https://github.com/uncomplicate/deep-diamond/blob/master/tes... [2] - https://github.com/uncomplicate/deep-diamond/blob/master/tes...

Let me chip in with some self-promotion. This book explains and executes every single line of code interactively, from low level operations to high-level networks that do everything automatically. The code is built on the state of the art performance operations of oneDNN (Intel, CPU) and cuDNN (CUDA, GPU). Very concise readable and understandable by humans. https://aiprobook.com/deep-learning-for-programmers/ Here's the open source library built throughout the book: https://github.com/uncomplicate/deep-diamond Some chapters from the beginning of the book are available on my blog, as a tutorial series: https://dragan.rocks

You can subscribe to Dragan Djuric's deep learning book [1]. He is using the subscriptions to fund work on Deep Diamond [2], a fast Deep Learning framework with GPU support for Clojure. [1] https://aiprobook.com/deep-learning-for-programmers/ [2] https://github.com/uncomplicate/deep-diamond