Backpropagation from Scratch in Python скачать в хорошем качестве

Backpropagation from Scratch in Python

👨‍💻 to get started with AI engineering, check out this Scrimba course: https://scrimba.com/the-ai-engineer-p... In this video we will learn how to code the backpropagation algorithm from scratch in Python (Code provided!) Excellent Backpropagation Tutorial: https://mattmazur.com/2015/03/17/a-st... Credit for the videos used: https://losslandscape.com/videos/ Credit for the song used: Sunshine on Sand (available on Youtube music) Github link to the code: https://github.com/yacineMahdid/artif... Previous video on backpropagation theory:    • Learn Backpropagation Derivation Step by Step   Previous video on forward propagation:    • Understanding Forward Propagation in Neura...   Previous video on perceptron:    • Create a Perceptron from Scratch in Python   Previous video on gradient descent:    • Gradient Descent from Scratch in Python   Here is some information from our beloved Wikipedia on what Backpropagation is: " In machine learning, backpropagation (backprop, BP) is a widely used algorithm in training feedforward neural networks for supervised learning. Generalizations of backpropagation exist for other artificial neural networks (ANNs), and for functions generally – a class of algorithms referred to generically as "backpropagation". In fitting a neural network, backpropagation computes the gradient of the loss function with respect to the weights of the network for a single input–output example, and does so efficiently, unlike a naive direct computation of the gradient with respect to each weight individually. This efficiency makes it feasible to use gradient methods for training multilayer networks, updating weights to minimize loss; gradient descent, or variants such as stochastic gradient descent, are commonly used. The backpropagation algorithm works by computing the gradient of the loss function with respect to each weight by the chain rule, computing the gradient one layer at a time, iterating backward from the last layer to avoid redundant calculations of intermediate terms in the chain rule; this is an example of dynamic programming. The term backpropagation strictly refers only to the algorithm for computing the gradient, not how the gradient is used; but the term is often used loosely to refer to the entire learning algorithm, including how the gradient is used, such as by stochastic gradient descent. Backpropagation generalizes the gradient computation in the delta rule, which is the single-layer version of backpropagation, and is in turn generalized by automatic differentiation, where backpropagation is a special case of reverse accumulation (or "reverse mode"). The term backpropagation and its general use in neural networks was announced in Rumelhart, Hinton & Williams (1986a), then elaborated and popularized in Rumelhart, Hinton & Williams (1986b), but the technique was independently rediscovered many times, and had many predecessors dating to the 1960s; see § History. A modern overview is given in the deep learning textbook by Goodfellow, Bengio & Courville (2016). " ---- Join the Discord for general discussion:   / discord   ---- Follow Me Online Here: Twitter:   / codethiscodeth1   GitHub: https://github.com/yacineMahdid LinkedIn:   / yacine-mahdid-809425163   Instagram:   / yacine_mahdid   ___ Have a great week! 👋