Workshop 1 - Tensorflow BasicsLearn about computation graph, variable, placeholder and matrix.
Learn about computation graph, variable, placeholder and matrix.
Workshop 2 - Simple Linear RegressionPerform linear regression in a very simple problem domain. The goal is to learn how linear regression works.
Perform linear regression in a very simple problem domain. The goal is to learn how linear regression works.
Workshop 3 - AirBnB Property Price PredictionThis is a realistic regression problem. We try to predict property rental prices in the Boston area. We learn to work with categorical features like neighborhood and property type.
This is a realistic regression problem. We try to predict property rental prices in the Boston area. We learn to work with categorical features like neighborhood and property type.
Workshop 4 - Build a Simple Neural NetworkWe will learn to build a basic neural network to solve a very simple problem. Again, the goal here is to understand the fundamentals of NN.
We will learn to build a basic neural network to solve a very simple problem. Again, the goal here is to understand the fundamentals of NN.
Workshop 5 - AirBnB Property Price Prediction Using Neural NetworkWe now use NN to solve this problem with higher accuracy.
We now use NN to solve this problem with higher accuracy.
Workshop 6 - Basic Binary Linear Logistic RegressionWe learn to do binary classification using linear logistic regression.
We learn to do binary classification using linear logistic regression.
Workshop 7 - Titanic Survivability PredictionThis is a more realistic binary classification problem. This also uses categorical features like class of travel (first class, second class etc.).
This is a more realistic binary classification problem. This also uses categorical features like class of travel (first class, second class etc.).
Workshop 8 - Multi-class Linear Logistic RegressionWe learn to classify among multiple classes. This workshop uses fetal heart monitoring (Cardiotocography) to predict complications during childbirth.
We learn to classify among multiple classes. This workshop uses fetal heart monitoring (Cardiotocography) to predict complications during childbirth.
Workshop 9 - Multi-class Logistic Regression Using neural NetworkWe solve the same fetal heart monitoring problem using neural network. This gives us much higher accuracy.
We solve the same fetal heart monitoring problem using neural network. This gives us much higher accuracy.
Workshop 9 - Basic Convolutional Neural Network (CNN)The goal of this workshop is the understand the structure of a CNN. We learn about the convolution layer, max pooling layer, fully connected layer and readout layer. We solve the MNIST handwritten digit comprehension problem.
The goal of this workshop is the understand the structure of a CNN. We learn about the convolution layer, max pooling layer, fully connected layer and readout layer. We solve the MNIST handwritten digit comprehension problem.
Workshop 10 - See Convolution in ActionWe go deeper into CNN. We actually observe how convolution works.
We go deeper into CNN. We actually observe how convolution works.
Workshop 11 - Solve CIFAR-10 ChallengeCIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes. We train a CNN that tries to classify images in those 10 classes.
CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes. We train a CNN that tries to classify images in those 10 classes.