This talk will cover the basics of building neural networks for software engineers, through neural weights and biases, activation functions, supervised learning, and gradient descent. I'll show you some tips and best practices for effective training, such as learning rate decay, gradient descent regularization, and the subtleties of overfitting. Be aware that dense and convolutional neural networks are key to any modern implementation. This session starts with low-level Tensorflow and also includes a sample of high-level Tensorflow code using layers and data sets.

Virtual link 

D4-2011 (UdeS)