Project 1: Bike Share Optimization

Click here to view the notebook - In this project, we’ll get to build a neural network from scratch to carry out a prediction problem on a real dataset! By building a neural network from the ground up, we’ll have a much better understanding of gradient descent, backpropagation, and other concepts that are important to know before we move to higher level tools such as Tensorflow. You’ll also get to see how to apply these networks to solve real prediction problems!

Project 2: Image Classification

Click here to view the notebook - In this project, we’ll classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects. The dataset will need to be preprocessed, then train a convolutional neural network on all the samples. we’ll normalize the images, one-hot encode the labels, build a convolutional layer, max pool layer, and fully connected layer. At the end, you’ll see their predictions on the sample images.

Project 3: TV-Script-Generation

Click here to view the notebook - In this project, we’ll generate our own Simpsons TV scripts using RNNs. we’ll be using part of the Simpsons dataset of scripts from 27 seasons. The Neural Network we’ll build will generate a new TV script for a scene at Moe’s Tavern.

Project 4: language-translation

Click here to view the notebook - In this project, we will take a peek into the realm of neural network machine translation. we’ll be training a sequence to sequence model on a dataset of English and French sentences that can translate new sentences from English to French.