Introduction I recently interviewed with a startup that’s tackling the inventory demand forecasting problem, whereby retailers have to forecast the future demand for their products, so they know how much inventory to purchase today. During the interview, I got asked a question that would consume me for the following week. “How would you approach the size curve problem?” That problem goes like this.. The Size Curve Problem Most retailers sell a catalog of products, where each product has a collection of variants.

Neural Networks For Your Dog Wanna understand neural networks so well you could code them from scratch? Then check out my course, where we do exactly that! Course Curriculum (See the code on GitHub) Introduction 1.1 Introduction Perceptron 2.1 MNIST Dataset 2.2 Perceptron Model 2.3 Perceptron Learning Algorithm 2.4 Pocket Algorithm 2.5 Multiclass Support 2.6 Perceptron To Neural Network Neural Network 3.1 Simple Images 3.2 Random Weights 3.3 Gradient Descent

1.1 Introduction Hey, thanks for checking out my course - Neural Networks For Your Dog, so easy your dog could learn them! Course Curriculum (See the code on GitHub) Introduction 1.1 Introduction Perceptron 2.1 MNIST Dataset 2.2 Perceptron Model 2.3 Perceptron Learning Algorithm 2.4 Pocket Algorithm 2.5 Multiclass Support 2.6 Perceptron To Neural Network Neural Network 3.1 Simple Images 3.2 Random Weights 3.3 Gradient Descent 3.4 Multiclass Support

2.1 MNIST Dataset In this lecture, we’ll check out the MNIST dataset - a dataset of handwritten digits - which we’ll use to motivate our construction of an image classification neural network model. Code Course Curriculum (See the code on GitHub) Introduction 1.1 Introduction Perceptron 2.1 MNIST Dataset 2.2 Perceptron Model 2.3 Perceptron Learning Algorithm 2.4 Pocket Algorithm 2.5 Multiclass Support 2.6 Perceptron To Neural Network Neural Network

2.2 Perceptron Model In this lecture, we’ll discuss and code up a Perceptron - a linear binary classifier created by Frank Rosenblatt in 1958. Code Course Curriculum (See the code on GitHub) Introduction 1.1 Introduction Perceptron 2.1 MNIST Dataset 2.2 Perceptron Model 2.3 Perceptron Learning Algorithm 2.4 Pocket Algorithm 2.5 Multiclass Support 2.6 Perceptron To Neural Network Neural Network 3.1 Simple Images 3.2 Random Weights

2.3 Perceptron Learning Algorithm In this lecture, we discuss and code up the classical Perceptron learning algorithm. Code Course Curriculum (See the code on GitHub) Introduction 1.1 Introduction Perceptron 2.1 MNIST Dataset 2.2 Perceptron Model 2.3 Perceptron Learning Algorithm 2.4 Pocket Algorithm 2.5 Multiclass Support 2.6 Perceptron To Neural Network Neural Network 3.1 Simple Images 3.2 Random Weights 3.3 Gradient Descent 3.4 Multiclass Support