xREL.v3

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PROUDLY PRESENTING:

Foundations.of.PyTorch

INFORMATION:

Date............: 2019-04-02
Rars............: 15
Course Length...: 2 hrs 51 mins
Website.........: https://www.pluralsight.com

Release Notes...:

PyTorch is fast emerging as a popular choice for building deep
learning models owing to its flexibility, ease-of-use and
built-in support for optimized hardware such as GPUs. Using
PyTorch, you can build complex deep learning models, while
still using Python-native support for debugging and
visualization.  In this course, Foundations of PyTorch, you
will gain the ability to leverage PyTorch support for dynamic
computation graphs, and contrast that with other popular
frameworks such as TensorFlow.  First, you will learn the
internals of neurons and neural networks, and see how
activation functions, affine transformations, and layers come
together inside a deep learning model. Next, you will
discover how such a model is trained, that is, how the best
values of model parameters are estimated.  You will then see
how gradient descent optimization is smartly implemented to
optimize this process. You will understand the different
types of differentiation that could be used in this process,
and how PyTorch uses Autograd to implement reverse-mode
auto-differentiation. You will work with different PyTorch
constructs such as Tensors, Variables, and Gradients.
Finally, you will explore how to build dynamic computation
graphs in PyTorch. You will round out the course by
contrasting this with the approaches used in TensorFlow,
another leading deep learning framework which previously
offered only static computation graphs, but has recently
added support for dynamic computation graphs.  When you re
finished with this course, you will have the skills and
knowledge to move on to building deep learning models in
PyTorch and harness the power of dynamic computation graphs.

Install Notes...:
Unrar, Learn and Enjoy!

GREETINGS:

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