ANNs and DNNs 0 to 100 | Python | Machine Learning | AI

Explore how linear classifiers use weight vectors, a bias, and dot products to compare CIFAR-10 input images with class templates, and train with cost functions via gradient descent.

Explore the multi-class SVM cost function to evaluate and optimize weight vectors, using scores for each class, margins, and hinge-like loss to maximize the correct class score.

Explore how cost functions assess parameter quality and how regularization limits overfitting by penalizing large weights using L1, L2, soft L1, and elastic net.

Turn the classifier scores into positive probabilities that sum to one with the softmax function, enabling focus on the correct class and one-hot encoding.

Derive the gradient of the cost function with respect to the weight vectors and move opposite to the gradient to minimize it, noting random search on Cifar10 yields 15.5% accuracy.

Explore the gradient descent algorithm to minimize a cost function by computing gradients with respect to weights and updating in the opposite direction, including batch, mini-batch, stochastic variants, and backpropagation.

Explore computational graphs and the backpropagation algorithm: compute loss and gradients via forward and backward passes, with data loss, regularization, nodes, topological order, and gradient rules.

Explore multi-layer artificial neural networks, from forward passes with nonlinear activations to backpropagated gradients, and learn to combat overfitting with regularization and cross-validation.

Explore how ReLU and its variants like leaky ReLU and max out address gradient and dead neuron problems, while preprocessing, Xavier initialization, batch normalization, and whitening improve deep network training.

Design the CIFAR-10 neural network with 3072 inputs, ten outputs, and a 50-neuron first hidden layer, then tune alpha and lambda via coarse-to-fine search and SVM loss.

Explore advanced optimization methods for training neural networks, including momentum, Nesterov's method, Adagrad, Rmsprop, and Adam, with insights on learning rate, velocity, and backpropagation.

Dropout combats overfitting by randomly zeroing a fraction of hidden-layer outputs during training, using masks and scaling; it is not applied in the output layer or test phase.

Learn how convolutional neural networks extract features from images through layered convolution, ReLU, padding, stride, and pooling, producing feature maps for classification.