datahacker.rs@gmail.com

Category: Deep Learning

# TF Implementing a VGG-19 network in TensorFlow 2.0

07.05.2020

Highlights: In this post we will show how to implement a fundamental Convolutional Neural Network like \(VGG-19\) in TensorFlow. The VGG-19 architecture was design by Visual Geometry Group, Department of Engineering Science, University of Oxford. It competed in the ImageNet Large Scale Visual Recognition Challenge in 2014. Tutorial Overview: Theory recapitulation Implementation in TensorFlow 1. Theory recapitulation With ConvNets becoming more of a popular in the computer vision field, a number of attempts have been made to improve the…
Read more

#015 TF Implementing AlexNet in TensorFlow 2.0

29.02.2020

Highlights: In this post we will show how to implement a fundamental Convolutional Neural Network \(AlexNet\) in TensorFlow 2.0. The AlexNet architecture is designed by Alex Krizhevsky and published with Ilya Sutskever and Geoffrey Hinton. It competed in the ImageNet Large Scale Visual Recognition Challenge in 2012. Tutorial Overview: Review of the Theory Implementation in TensorFlow 2.0 1. Review of the Theory Real life Computer Vision problems requires big amount of quality data to be trained on. In the past, people…
Read more

#014 TF Implementing LeNet-5 in TensorFlow 2.0

29.02.2020

Highlights: In this post we will show how to implement a foundamental Convolutional Neural Network like \(LeNet-5\) in TensorFlow. The LeNet-5 architecture was invented by Yann LeCun in 1998 and was the first Convolutional Neural Network. Tutorial Overview: Theory recapitulation Implementation in TensorFlow 1. Theory recapitulation The goal of \(LeNet-5 \) was to recognize handwritten digits. So, it takes as an input \(32\times32\times1 \) image. It is a grayscale image, thus the number of channels is \(1 \).…
Read more

#012 TF Transfer Learning in TensorFlow 2.0

16.02.2020

Highlights: In this post we are going to show how to build a computer vision model without building it from scratch. The idea behind transfer learning is that a neural network that has been trained on a large dataset can apply its knowledge to a dataset that it has never seen before. That is, why it’s called a transfer learning; we transfer the learning of an existing model to a new dataset. Tutorial Overview: Introduction Transfer…
Read more

#011 TF How to improve the model performance with Data Augmentation?

16.02.2020

Highlights: In this post we will show the benefits of data augmentation techniques as a way to improve performance of a model. This method will be very beneficial when we do not have enough data at our disposal. Tutorial Overview: Training without data augmentation What is data augmentation? Training with data augmentation Visualization 1. Training without data augmentation A familiar question is “why should we use data augmentation?”. So, let’s see the answer. In order…
Read more

Social Media
Visit Us On TwitterVisit Us On FacebookVisit Us On Linkedin

©  2020 Master Data Science. Built using WordPress and the Mesmerize Theme