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An interview with Anthony Lowhur – Recognizing 10,000 Yugioh Cards with...
In this blog post, I interview computer vision and deep learning engineer, Anthony Lowhur. Anthony shares the algorithms and techniques that he used to build a computer vision and deep learning system...
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Defending against adversarial image attacks with Keras and TensorFlow
In this tutorial, you will learn how to defend against adversarial image attacks using Keras and TensorFlow. So far, you have learned how to generate adversarial images using three different methods:...
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Mixing normal images and adversarial images when training CNNs
In this tutorial, you will learn how to generate image batches of (1) normal images and (2) adversarial images during the training process. Doing so improves your model’s ability to generalize and...
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An interview with Jagadish Mahendran, 1st place winner of the OpenCV Spatial...
In this post, I interview Jagadish Mahendran, senior Computer Vision/Artificial Intelligence (AI) engineer who recently won 1st place in the OpenCV Spatial AI Competition using the new OpenCV AI Kit...
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An interview with Gary Song, deep learning practitioner at Unity Technologies
In this blog post, I interview Gary Song, a deep learning practitioner at Unity Technologies. We’re now at the one-year anniversary of COVID-19. It’s been a particularly rough year for all of us. For...
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The Deep Learning Classification Pipeline
Based on our previous two sections on image classification and types of learning algorithms, you might be starting to feel a bit steamrolled with new terms, considerations, and what looks to be an...
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Image Classification Basics
A picture is worth a thousand words.— English idiom We’ve heard this adage countless times in our lives. It simply means that a complex idea can be conveyed in a single image. Whether examining the...
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What is Deep Learning?
Deep learning methods are representation-learning methods with multiple levels of representation, obtained by composing simple but nonlinear modules that each transform the representation at one level...
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Face detection with dlib (HOG and CNN)
In this tutorial, you will learn how to perform face detection with the dlib library using both HOG + Linear SVM and CNNs. The dlib library is arguably one of the most utilized packages for face...
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Face detection tips, suggestions, and best practices
In this tutorial, you will learn my tips, suggestions, and best practices to achieve high face detection accuracy with OpenCV and dlib. We’ve covered face detection four times on the PyImageSearch...
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Gradient Descent Algorithms and Variations
In this tutorial, you will learn: What gradient descent isHow gradient descent enables us to train neural networksVariations of gradient descent, including Stochastic Gradient Descent (SGD)How SGD can...
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Understanding weight initialization for neural networks
In this tutorial, we will discuss the concept of weight initialization, or more simply, how we initialize our weight matrices and bias vectors. This tutorial is not meant to be a comprehensive...
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Introduction to Neural Networks
We’ll study the fundamentals of neural networks in depth. We’ll start with a discussion of artificial neural networks and how they are inspired by the real-life biological neural networks in our own...
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Implementing feedforward neural networks with Keras and TensorFlow
Now that we have implemented neural networks in pure Python, let’s move on to the preferred implementation method — using a dedicated (highly optimized) neural network library such as Keras. Today, I...
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The four key ingredients when training any neural network
You might have started to notice a pattern in our Python code examples when training neural networks. There are four main ingredients you need to put together in your own neural network and deep...
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Backpropagation from scratch with Python
Backpropagation is arguably the most important algorithm in neural network history — without (efficient) backpropagation, it would be impossible to train deep learning networks to the depths that we...
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Implementing the Perceptron Neural Network with Python
First introduced by Rosenblatt in 1958, The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain is arguably the oldest and most simple of the ANN algorithms....
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Are CNNs invariant to translation, rotation, and scaling?
Are CNNs Invariant to Translation, Rotation, and Scaling? A common question I get asked is: Are Convolutional Neural Networks invariant to changes in translation, rotation, and scaling? Is that why...
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Convolutional Neural Networks (CNNs) and Layer Types
CNN Building Blocks Neural networks accept an input image/feature vector (one input node for each entry) and transform it through a series of hidden layers, commonly using nonlinear activation...
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Convolution and cross-correlation in neural networks
Our entire review of machine learning and neural networks thus far has been leading up to this point: understanding Convolutional Neural Networks (CNNs) and the role they play in deep learning. In...
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