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Category: Machine Learning

#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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#007 TF 2.0 An implementation of a Shallow Neural Network in tf.keras – Spirals dataset

Highlights: In this post we will see how we can classify a spirals dataset with a shallow neural network implemented in TensorFlow 2.0. Tutorial Overview: Imports and Dataset preparation Building a Neural Network Visualization 1. Dataset: import and preparation Let’s start by importing libraries that we will need in our code. Last time we were using a function from sklearn to create a dataset. Now we are going to make our dataset from scratch. First,…
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