Скачать с ютуб Reverse Mode Autodiff in Python (general compute graph) в хорошем качестве

Reverse Mode Autodiff in Python (general compute graph)

Let's implement automatic differentiation (=backpropagation) for a general directed acyclic compute graph to compute the gradient of a scalar-valued loss function. Here is the notebook: https://github.com/Ceyron/machine-lea... ------- 👉 This educational series is supported by the world-leaders in integrating machine learning and artificial intelligence with simulation and scientific computing, Pasteur Labs and Institute for Simulation Intelligence. Check out https://simulation.science/ for more on their pursuit of 'Nobel-Turing' technologies (https://arxiv.org/abs/2112.03235 ), and for partnership or career opportunities. ------- 📝 : Check out the GitHub Repository of the channel, where I upload all the handwritten notes and source-code files (contributions are very welcome): https://github.com/Ceyron/machine-lea... 📢 : Follow me on LinkedIn or Twitter for updates on the channel and other cool Machine Learning & Simulation stuff:   / felix-koehler   and   / felix_m_koehler   💸 : If you want to support my work on the channel, you can become a Patreon here:   / mlsim   🪙: Or you can make a one-time donation via PayPal: https://www.paypal.com/paypalme/Felix... ------- Timestamps: 00:00 Intro 01:30 Describe a function by a compute graph 02:45 Symbolic Derivative 02:57 Implementing analytic functions 04:00 Manual definition of compute graph 06:23 A (primal) function library 07:42 Primal forward execution of the compute graph 13:38 Library of primitive with pullbacks 19:40 Implementing reverse-mode AD via a vJp transformation 27:50 Testing the AD 29:07 Syntactic Sugar 30:39 Outro