-
Deep Feature Synthesis: How Automated Feature Engineering Works
Automating feature engineering optimizes the process of building and deploying accurate machine learning models by handling necessary but tedious tasks so data scientists can focus more on other important steps.
-
 A Simple Starter Guide to Build a Neural Network
This guide serves as a basic hands-on work to lead you through building a neural network from scratch. Most of the mathematical concepts and scientific decisions are left out.
-
Understanding Learning Rates and How It Improves Performance in Deep Learning
Furthermore, the learning rate affects how quickly our model can converge to a local minima (aka arrive at the best accuracy). Thus getting it right from the get go would mean lesser time for us to train the model.
-
Data Structures Related to Machine Learning Algorithms
If you want to solve some real-world problems and design a cool product or algorithm, then having machine learning skills is not enough. You would need good working knowledge of data structures.
-
How To Grow As A Data Scientist
In order for a data scientist to grow, they need to be challenged beyond the technical aspects of their jobs. They need to question their data sources, be concise in their insights, know their business and help guide their leaders.
-
Training and Visualising Word Vectors
In this tutorial I want to show how you can implement a skip gram model in tensorflow to generate word vectors for any text you are working with and then use tensorboard to visualize them.
-
Using Genetic Algorithm for Optimizing Recurrent Neural Networks
In this tutorial, we will see how to apply a Genetic Algorithm (GA) for finding an optimal window size and a number of units in Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN).
-
Learn Data Science Without a Degree
But how do you learn data science? Let’s take a look at some of the steps you can take to begin your journey into data science without needing a degree, including Springboard’s Data Science Career Track.
-
Propensity Score Matching in R
Propensity scores are an alternative method to estimate the effect of receiving treatment when random assignment of treatments to subjects is not feasible.
-
Gradient Boosting in TensorFlow vs XGBoost
For many Kaggle-style data mining problems, XGBoost has been the go-to solution since its release in 2016. It's probably as close to an out-of-the-box machine learning algorithm as you can get today.
|
A Simple Starter Guide to Build a Neural Network
Data Structures Related to Machine Learning Algorithms