Overfitting occurs when the model performs well on training data but generalizes poorly to unseen data. Overfitting is a very common problem in Machine Learning and there has been an extensive range of literature dedicated to studying methods for preventing overfitting. In the following, I’ll describe eight simple approaches to alleviate overfitting by introducing only one change to the data, model, or learning algorithm in each approach.

Training Deep Neural Networks – mc.ai

Table of Contents

1. Hold-out
2. Cross-validation
3. Data augmentation
4. Feature selection
5. L1 / L2 regularization
6. Remove layers / number of units per layer
7. Dropout
8. Early stopping

1. Hold-out (data)

Rather than using all…


Waymo’s self-driving taxi service just hit the road this month — but how do autonomous vehicles even work? The lines drawn on roads indicate to human drivers where the lanes are and act as a guiding reference to which direction to steer the vehicle accordingly and convention to how vehicle agents interact harmoniously on the road. Likewise, the ability to identify and track lanes is cardinal for developing algorithms for driverless vehicles.

In this tutorial, we will learn how to build a software pipeline for tracking road lanes using computer vision techniques. …


Fidget spinner classifier

In this tutorial, we will learn how to build a custom real-time object classifier to detect any object of your choice! We will be using BeautifulSoup and Selenium to scrape training images from Shutterstock, Amazon’s Mechanical Turk (or BBox Label Tool) to label images with bounding boxes, and YOLOv3 to train our custom detection model.

Pre-requisites:

1. Linux
2. CUDA supported GPU (Optional)

Table of Contents:

Step 1: Scraping
Step 2: Labeling
Step 3: Training

Step 1: Scraping

In Step 1, we will be using shutterscrape.py, a small Python program I wrote, to help us batch download training images from Shutterstock.

1. Installing ChromeDriver

Open Terminal, download the ChromeDriver zip…

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