In this video we start by walking through some of the basics. We look at why we use neural networks and how they function. We do an overview of network architecture (input layer, hidden layers, output layer). We talk a bit about how you choose how many hidden layers and neurons to have. We also look at hyperparameters like batch size, learning rate, optimizers (adam), activation functions (relu, sigmoid, softmax), and dropout. We finish the first section of the video talking a little about the differences between keras, tensorflow, & pytorch.

Next, we jump into some coding examples to classify data with neural nets. In this section we load in data, do some processing, build our network, fit our data to it, and then finally evaluate our model…

To install Tensorflow, download Anaconda: https://docs.anaconda.com/anaconda/in… ​

Data & code used in tutorial: https://github.com/KeithGalli/neural-…

Subscribe : https://www.youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw

#python #tensorflow #keras