Put your understanding of deep learning into practice with (one of) the most flexible deep learning framework.
The tutorial will show you how to implement a neural network along with some theoretical reminders, practical tips, and suggested code workflows. More precisely, we will explore how PyTorch can be used as a simple version of numpy, with GPU acceleration and automatic gradients computations. Afterwards, will explore how PyTorch can be used to train a neural network in an efficient way, with minimum code overhead. We'll used it to train a convolutional model for digit recognition.
Keywords: Tensors, Python, GPU, Automatic Differentiation, Neural Networks, Dynamic
● Python, numpy
● Machine learning basics: loss function, generalization
● Neural networks understanding: feed forward, backpropagation, gradient descent
Required: A computer (GPU isn't required) and a Google account
The tutorial will last until 17:00 PM for those who wishes to stay.
Registration at: https://goo.gl/forms/sb33ufXIVkIgCJ3e2