Some things I’d like you to know about Data Science

Some things I’d li...

Things I’ve learned mostly by making mistakes Masses of data + cutting edge machine learning + cheap compute = Profit. Right? It’s not that simple. Data science isn’t a replacement for asking difficult questions and doing hard work based on the answers. In fact, it’s quite the opposite. Enabled by increasingly powerful algorithms and ever larger […]

Understanding and optimizing neural network hyperparameters part 3: Other parameters, and optimizing

Understanding and op...

Understanding and Optimizing Neural Network Hyperparameters Series The learning rate The Neurons Other parameters, and optimizing   In the other parts I have explained the main hyperparameters used to train a neural network, and how they can contribute to the networks success. However, optimizing these values can be tedious. Once the correct combination of all […]

Understanding and Optimizing Neural Network Hyperparameters part 2: The Neurons

Understanding and Op...

Intro In this part 2 will discuss how neural nets are structured, how the structure can affect the success of a model, and why. The actual design of a neural network is very much determined by the data that’s supplied. You will already know how outside layers are formed, where each bit of data is […]

Understanding and optimizing neural network hyperparameters part 1: The learning rate

Understanding and op...

Introduction to the series When first trying to understand a neural network, one of the most debated and perhaps mysterious aspects of them are the parameters that contribute to their success. These parameters are for you to ultimately decide. As it stands, they tend to be determined by obvious methods of tuning, and trial and […]

Yet another introduction to Neural Networks

Yet another introduc...

In this notebook, I will explain how to implement a neural network from scratch and use the version of MNIST dataset that is provided within Scikit-Learn for testing. I will specificallty illustrate the use of Python classes to define layers in the network as objects. Each layer object has forward and backward propagation methods which […]

Learning Deep Learning Part 2: Online Courses

Learning Deep Learni...

Intro. How I Plan to Teach Myself Deep Learning Using Only Free Resources Learning Deep Learning Series Part 1: Videos Learning Deep Learning Part 2: Online Courses Learning Deep Learning Part 3: Github Repos This is the second in a series of articles in which Data Science Associate George McIntire catalogs his experience teaching himself […]

Intro to Caret, Model Training and Tuning

Intro to Caret, Mode...

Contents Model Training and Parameter Tuning An Example Basic Parameter Tuning Notes on Reproducibility Customizing the Tuning Process Pre-Processing Options Alternate Tuning Grids Plotting the Resampling Profile The trainControl Function Alternate Performance Metrics Choosing the Final Model Extracting Predictions and Class Probabilities Exploring and Comparing Resampling Distributions Within-Model Between-Models Fitting Models Without Parameter Tuning 5.1 Model Training and […]

Intro to Caret: Data Splitting

Intro to Caret: Data...

Contents Simple Splitting Based on the Outcome Splitting Based on the Predictors Data Splitting for Time Series Data Splitting with Important Groups 4.1 Simple Splitting Based on the Outcome The function createDataPartition can be used to create balanced splits of the data. If the yargument to this function is a factor, the random sampling occurs within each class and […]

Principal Component Analysis Tutorial

Principal Component ...

The Problem Imagine that you are a nutritionist trying to explore the nutritional content of food. What is the best way to differentiate food items? By vitamin content? Protein levels? Or perhaps a combination of both? Knowing the variables that best differentiate your items has several uses: 1. Visualization. Using the right variables to plot […]