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Category: Deep Learning

#015 TF Implementing AlexNet in TensorFlow 2.0

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…
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#014 TF Implementing LeNet-5 in TensorFlow 2.0

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 \(1 \).…
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#012 TF Transfer Learning in TensorFlow 2.0

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

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…
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#009 TF An implementation of a Convolutional Neural Network in tf.keras – MNIST dataset

In this post we will see how we can classify handwritten digits using Convolutional Neural Network implemented in TensorFlow 2.0. Required packages: Numpy Matplotlib Tensorflow Sklearn Seaborn Table of Contents: Load the digit dataset Implementing a Neural Network Visualization and Testing 1. Load the digit dataset Let start with importing all necessary libraries. After imports, we can use imported module to load mnist data. The load_data() function will automatically download and split our data into…
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