Grasp deep studying in PyTorch utilizing an experimental scientific strategy, with a lot of examples and apply issues.
What you’ll be taught
The speculation and math underlying deep studying
construct synthetic neural networks
Architectures of feedforward and convolutional networks
Constructing fashions in PyTorch
The calculus and code of gradient descent
Nice-tuning deep community fashions
Study Python from scratch (no prior coding expertise mandatory)
How and why autoencoders work
use switch studying
Bettering mannequin efficiency utilizing regularization
Optimizing weight initializations
Perceive picture convolution utilizing predefined and discovered kernels
Whether or not deep studying fashions are comprehensible or mysterious black-boxes!
Utilizing GPUs for deep studying (a lot sooner than CPUs!)
English
language
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