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Tag: deep neural network

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

In this post we will learn how to make a classification of Moons dataset with a shallow Neural network. The Neural Net we will implemented in TensorFlow 2.0 using Keras API. With the following code we are going to import all libraries that we will need. First, we will generate a random dataset, then we will split it into train and test set. We will also print dimensions of these datasets. With the following code…
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#005 TF 2.0 An implementation of a Shallow Neural Network with tf.keras – Circles dataset

Highlights: In previous post we have talked about TensorFlow Wrappers and there we concluded that tf.keras is the most convenient way to build neural networks. Now we are going to implement one very simple network using this high-level API. Tutorial Overview: Imports and Dataset preparation Building a Neural Network Visualization 1. Imports and Dataset preparation Let’s start with basic imports. Don’t worry if some things are not familiar with all of these libraries, we will…
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#OD1 YOLO Object Detection

YOU ONLY LOOK ONCE Highlights: In this post we will learn about the YOLO Object Detection system, and how to implement such a system with Keras. About Yolo: Our unified architecture is extremely fast. Our base YOLO model processes imagesin real-time at 45 frames per second. A smaller version of the network, Fast YOLO,processes an astounding 155 frames per second … — You Only Look Once: Unified, Real-Time Object Detection, 2015 Tutorial Overview: This post…
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