- Exploring the SwAV Method - Jul 9, 2021.
This post discusses the SwAV (Swapping Assignments between multiple Views of the same image) method from the paper “Unsupervised Learning of Visual Features by Contrasting Cluster Assignments” by M. Caron et al.
- How to Build An Image Classifier in Few Lines of Code with Flash - Jul 7, 2021.
Introducing Flash: The high-level deep learning framework for beginners.
- Fine-Tuning Transformer Model for Invoice Recognition - Jun 23, 2021.
The author presents a step-by-step guide from annotation to training.
- Build Dog Breeds Classifier Step By Step with AWS Sagemaker - Jun 17, 2020.
This post takes you through the basic steps for creating a cloud-based deep learning dog classifier, with everything accomplished from the AWS Management Console.
- Few-Shot Image Classification with Meta-Learning - Mar 12, 2020.
Here is how you can teach your model to learn quickly from a few examples.
- The Augmented Scientist Part 1: Practical Application Machine Learning in Classification of SEM Images - Mar 3, 2020.
Our goal here is to see if we can build a classifier that can identify patterns in Scanning Electron Microscope (SEM) images, and compare the performance of our classifier to the current state-of-the-art.
- Deep Learning for Image Classification with Less Data - Nov 20, 2019.
In this blog I will be demonstrating how deep learning can be applied even if we don’t have enough data.
- Transfer Learning Made Easy: Coding a Powerful Technique - Nov 13, 2019.
While the revolution of deep learning now impacts our daily lives, these networks are expensive. Approaches in transfer learning promise to ease this burden by enabling the re-use of trained models -- and this hands-on tutorial will walk you through a transfer learning technique you can run on your laptop.
- A Single Function to Streamline Image Classification with Keras - Sep 23, 2019.
We show, step-by-step, how to construct a single, generalized, utility function to pull images automatically from a directory and train a convolutional neural net model.
- A 2019 Guide to Semantic Segmentation - Aug 12, 2019.
Semantic segmentation refers to the process of linking each pixel in an image to a class label. These labels could include a person, car, flower, piece of furniture, etc., just to mention a few. We’ll now look at a number of research papers on covering state-of-the-art approaches to building semantic segmentation models.
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- K-means Clustering with Dask: Image Filters for Cat Pictures - Jun 18, 2019.
How to recreate an original cat image with least possible colors. An interesting use case of Unsupervised Machine Learning with K Means Clustering in Python.
- 7 Steps to Mastering Intermediate Machine Learning with Python — 2019 Edition - Jun 3, 2019.
This is the second part of this new learning path series for mastering machine learning with Python. Check out these 7 steps to help master intermediate machine learning with Python!
- Boost Your Image Classification Model - May 27, 2019.
Check out this collection of tricks to improve the accuracy of your classifier.
- Large-Scale Evolution of Image Classifiers - May 16, 2019.
Deep neural networks excel in many difficult tasks, given large amounts of training data and enough processing power. The neural network architecture is an important factor in achieving a highly accurate model... Techniques to automatically discover these neural network architectures are, therefore, very much desirable.
- Neural Networks seem to follow a puzzlingly simple strategy to classify images - Mar 5, 2019.
We explain why state-of-the-art Deep Neural Networks can still recognize scrambled images perfectly well and how this helps to uncover a puzzlingly simple strategy that DNNs seem to use to classify natural images.
- How to do Everything in Computer Vision - Feb 27, 2019.
The many standard tasks in computer vision all require special consideration: classification, detection, segmentation, pose estimation, enhancement and restoration, and action recognition. Let me show you how to do everything in Computer Vision with Deep Learning!