Скачать с ютуб Deep learning optimization stochastic gradient descent explained в хорошем качестве

Deep learning optimization stochastic gradient descent explained

Download 1M+ code from https://codegive.com/1878632 okay, let's dive deep into stochastic gradient descent (sgd) and its variations for deep learning optimization. this will be a comprehensive tutorial covering the core concepts, math, practical considerations, and code examples in python using tensorflow and numpy. *1. the optimization problem in deep learning* *the goal:* the heart of deep learning is finding the optimal set of parameters (weights and biases) for a neural network that minimizes a **loss function**. the loss function quantifies how well the network is performing on a given task. lower loss means better performance. *loss function:* examples include: *mean squared error (mse):* used for regression tasks, measures the average squared difference between predicted and actual values. *cross-entropy:* used for classification tasks, measures the dissimilarity between predicted probability distributions and true distributions. *parameter space:* imagine a high-dimensional space where each axis represents a weight or bias in your neural network. the loss function defines a landscape over this space. the goal is to find the "valley" (the point with the lowest loss) in this landscape. *challenge:* this landscape is often complex, non-convex (meaning it has many local minima), and high-dimensional. finding the global minimum is generally impossible. we aim for a "good enough" local minimum. *2. gradient descent: the core idea* *gradient:* the gradient of the loss function tells you the direction of steepest ascent. it's a vector of partial derivatives, where each partial derivative represents how the loss changes with respect to a particular parameter. *the algorithm:* 1. *initialization:* start with random initial values for the network's parameters (weights and biases). 2. *compute the gradient:* calculate the gradient of the loss function with respect to the parameters. 3. *update parameters:* move the parameters i ... #DeepLearning #StochasticGradientDescent #performancetesting deep learning optimization stochastic gradient descent SGD machine learning neural networks backpropagation learning rate convergence loss function gradient descent variants mini-batch training overfitting regularization techniques adaptive learning rates