Use a pre-trained neural network for feature extraction and cluster images using K-means. In this tutorial, I'm going to walk you through using a pre-trained neural network to extract a feature vector from images and cluster the images based on how similar the feature vectors are.
In this blog, I’m going to talk about how I have gotten an accuracy greater than 88% (92% epoch 22) with Cifar-10 using transfer learning, I used VGG16 and I applied a very low constant learning rate and I implemented the function upsampling to get more data points for processing
So you think you don’t have enough data to do Machine Learning. Ask a beginner why ML is so difficult and you will most likely get an answer ... So you think you don't have enough data to do Machine Learning.
Lego Minifigure Gender Classification Using Deep Learning. With CNN’s and transfer learning
The article starts with a very high-level overview of how neural networks are built and then dives into how Transfer Learning works in the domain of computer vision. I’ll share a follow-up article on Transfer Learning in natural language processing (NLP) in the coming weeks.
Fastai - Disaster Prediction using ULMFiT. Transfer Learning’s application in the field of Natural Language Processing
I will give you an overview of the idea of transfer learning. This blog is divided into two parts and In this part, I will try to explain the theoretical concepts of different types of transfer learning techniques and how to store and use the feature vectors for making a pretty accurate image classifier.
Neural Architecture Transfer. NAT may be the Next Big Thing in Deep Learning
4 Pre-Trained CNN Models to Use for Computer Vision with Transfer Learning. Using State-of-the-Art Pre-trained Neural Network Models to Tackle Computer Vision Problems with Transfer Learning
In this article, I will discuss about transfer learning, the VGG model, and feature extraction. In the last section, I will demonstrate an interesting example of transfer learning where the transfer learning technique displays unexpectedly poor performance in classifying the Mnist digit dataset.
Hence, it is very important that our deep learning pipeline optimally utilizes all the available compute resources to make both the phases efficient by all means.
With TensorFlow 2.3, Amazon SageMaker Python SDK 2.5.x and Custom SageMaker Training & Serving Docker Containers
This article aims to help out beginners in machine learning on creating your own custom object detector. I have been trying to create a simple object detector and had to go through many articles spread across the internet to find all the required information. So I figured I’ll gather all the information I found in one place to make things easier for the next me.I’ll keep this as easy and informative as possible. How to train a custom object detector using Open Images dataset and TensorFlow Object Detection API
Develop a Convolutional Neural Network model and deploy it as a web application using Flask. With the training of deep learning models, how can we deploy the trained model as a web application? Enters Flask — the most popular web application framework for Python.
Transfer Learning is the process of taking a network pre-trained on a dataset and utilizing it to recognize the image, object detection, image segmentation, semantic segmentation, and many more. We can utilize the robust, discriminative filters learned by the state-of-the-art network on challenging datasets such as Imagenet/COCO and then apply these networks for some other tasks.
A step-by-step guide to classifying dog images amongst 115 breeds! Stuck behind the paywall? Click here to read the full story with my friend link!
Most of us find that it is very difficult to add additional layers and generate connections between the model and additional layers . But , here I am going to make it simple . So that , everyone can get benfit out of it . Just read this out once and we will be good to go .
Yet another week (4) of my 6 week ML Project just passed. Now, we are done and dusted with the most important and exciting task. YES, I mean training our model with the full data set.
Transfer learning is a method of reusing a pre-trained model knowledge for another task. It can be used for classification, regression and clustering problems.
Transfer learning is a research problem in machine learning that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem.