PyTorch for Deep Learning and Computer Vision

Explore derivatives and gradients in PyTorch by computing a polynomial's derivative at a point, using backward on a tensor with requires_grad, and extend to partial derivatives for multivariable functions.

Explore how a loss function guides fitting a line to data by adjusting the weight of a zero-bias model to minimize prediction error.

Explore the perceptron as the simplest neural network. Learn how back propagation, weights, and gradient descent enable neural networks to detect complex patterns.

Create a linearly separable dataset with Sklearn make_blobs, visualize it with matplotlib, and convert to PyTorch tensors to train a perceptron using gradient descent.

Implement a simple perceptron model in PyTorch by defining an nn.Module subclass, initializing with input and output sizes, applying a sigmoid, and plotting initial parameters with data.

train a two-input neural model on binary data using binary cross entropy loss and stochastic gradient descent in PyTorch, updating weights through forward predictions, backpropagation, and 1000 epochs.

Explore how neural networks combine linear models into nonlinear predictions using perceptrons and sigmoid activations. Learn how deep architectures stack hidden layers and weights to classify complex data.

Train deep neural networks by performing feedforward predictions, computing cross-entropy error, and backpropagating the loss to update weights with gradient descent across all layers.

Train a neural network to classify handwritten digits using a training dataset and a test set. Explore generalization, underfitting, overfitting, and regularization alongside validation sets to improve real world performance.

Learn to load and transform Mnist images in PyTorch using Torchvision, applying compose transforms to convert to tensors and normalize, and set up a training data loader.

Train and validate a neural network on the MNIST dataset using a validation loader to track validation loss and accuracy across epochs to observe generalization.

Explore the convolutional layer as the core building block of convolutional networks, learning features with small kernels, a stride, and feature maps, and apply relu activation to promote translational invariance.

Implement a PyTorch convolutional neural network to classify MNIST images using the Lynette model with two conv layers, pooling, and two fully connected layers.

Train a convolutional neural network in PyTorch on MNIST data using GPUs in Google Colab, monitor loss and accuracy, and apply dropout to reduce overfitting.

Apply transfer learning with PyTorch by using pre-trained AlexNet and VGG16, freezing feature extractors, and adapting the final layer for ants and bees dataset.