Kyosuke

Kyosuke

364. Bias-Variance Tradeoff

Tradeoffs When training a model, there is always a tradeoff between the model having bias and variance. When a model is suffering from high bias, it means that it is UNDER-fitting to the data and is not able to make…

363. Splitting Datasets

Training Your Model When training a model, the dataset is often divided into a Training set, a Validation Set, and a Test Set. The ratio to split the data into these 3 sets depends on how large your dataset is,…

362. Image Segmentation using K-means Clustering

Clustering Depending on your data and objective, you may not even have to train a deep-learning model for image segmentation. Here is one way to apply segmentation using Kmeans clustering. Implementation from sklearn.cluster import KMeans from matplotlib.image import imread import…

361. How Data Augmentation “Increases” Data

Data Augmentation Data Augmentation is a technique used to “increase” the amount of data to train a model. This can be helpful in cases such as when you don’t have a sufficient amount of data or when you want to…

360. Saving Checkpoint During Training

Implementation Saving checkpoints for model weights during training can be helpful in the case of the following examples. Want to resume training later Avoid losing weight data when the process stops during training due to some kind of error. Restore…

357. One-Shot Learning Basics

What It Does One-Shot learning can be useful when you want to identify someone just by giving the predicting model a single picture of them. Similarity Function A model can perform One-shot learning by learning a “similarity” function. When the…

356. Data Preprocessing Steps

The Main 4 Steps There are mainly 4 steps for data preprocessing. Data Quality Assessment Data Cleaning Data Transformation Data Reduction 1. Data Quality Assessment Before jumping into coding, evaluating the overall data quality is essential. Here are several problems…

355. Model-Based VS Instance-Based Learning

Overview Model-Based Learning Creates a function F(x) using the given data to predict the output. EX): Support Vector Machines Instance-Based Learning Uses the given data itself as the model. If an input is given, it will look through the current…