Hi, I am Tylan O'Flynn.
I work out of Edmonton, Canada.
I have been involved in several startups that use machine learning algorithms to solve novel problems.
My main focus is on reinforcement learning incorporating deep neural networks as function approximators. If you have any questions, feel free to contact me. Particularly good questions will get a video made about them!
Browse Tylan O'Flynn's Lessons
showing 12 lessons...
AvgPool2D - Use the PyTorch AvgPool2D Module to incorporate average pooling into a PyTorch neural network
BatchNorm2d - Use the PyTorch BatchNorm2d Module to accelerate Deep Network training by reducing internal covariate shift
Use the PyTorch view method to manage Tensor Shape within a Convolutional Neural Network
Construct A Custom PyTorch Model by creating your own custom PyTorch module by subclassing the PyTorch nn.Module class
Use PyTorch's nn.ReLU and add_module operations to define a ReLU layer
Use PyTorch nn.Sequential and PyTorch nn.Conv2d to define a convolutional layer in PyTorch
Use PyTorch's nn.Sequential and add_module operations to define a sequential neural network container
Augment the CIFAR10 Dataset Using the TorchVision RandomHorizontalFlip (transforms.RandomHorizontalFlip) and RandomCrop (transforms.RandomCrop) Transforms
Use the Torchvision Transforms Parameter in the initialization function to apply transforms to PyTorch Torchvision Datasets during the data import process
Use Torchvision Transforms Normalize (transforms.Normalize) to normalize CIFAR10 dataset tensors using the mean and standard deviation of the dataset
Convert CIFAR10 Dataset from PIL Images to PyTorch Tensors by Using PyTorch's ToTensor Operation
PyTorch CIFAR10 - Load CIFAR10 Dataset (torchvision.datasets.cifar10) from Torchvision and split into train and test data sets