Month: March 2020

Fashion Image classification (fashion_mnist) using a LaNet Architecture in Python

Fashion Image classification (fashion_mnist) using a LaNet Architecture in Python     Fashion image classification is a task of recognizing the type of clothing or accessory present in an image. The Fashion MNIST dataset is a commonly used dataset for this task, and it consists of images of clothing and accessories, along with their corresponding …

How to classify digits using a LaNet in Keras and Python

How to classify digits using a LaNet in Keras and Python     Classifying digits using a LeNet in Keras and Python is a straightforward process that involves several steps. In this essay, we will discuss these steps in detail. The first step is to prepare the data for the model. This typically involves loading …

How to build a LaNet using Keras and Python

How to build a LaNet using Keras and Python     LeNet is a convolutional neural network architecture designed to recognize handwritten digits. It was developed by Yann LeCun in the late 1990s and is considered a pioneer in the field of deep learning. In this essay, we will discuss how to build a LeNet …

How to save a deep learning model: A simple CNN model in Python for Image Classification

How to save a deep learning model: A simple CNN model in Python for Image Classification     Saving a deep learning model is an important step to ensure that the model can be used later on without having to retrain it from scratch. In this essay, we will discuss how to save a simple …

Learn_By_Example_Image_Augmentation_Part_2

  Learn by Doing With DataCamp   In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in Python programming: Learn_By_Example_Image_Augmentation_Part_1. What should I learn from this recipe? You will learn: How to code a keras and tensorflow model in …

Learn_By_Example_Image_Augmentation_Part_1

  In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in Python programming: Learn_By_Example_Image_Augmentation_Part_1. What should I learn from this recipe? You will learn: How to code a keras and tensorflow model in Python. How to setup a sequential …

How to classify images using CNN layers in Keras: An application of MNIST Dataset

How to classify images using CNN layers in Keras: An application of MNIST Dataset   Convolutional Neural Networks (CNNs) are a powerful tool for image classification tasks. They work by learning hierarchical representations of the image, where each layer captures a different level of abstraction. In this essay, we will discuss how to classify images …

How to setup CNN layers in Keras for image classification

How to setup CNN layers in Keras for image classification     Convolutional Neural Networks (CNNs) are a type of deep learning model that are particularly well-suited for image classification tasks. CNNs are designed to process data that has a grid-like topology, such as an image. They work by learning hierarchical representations of the image, …

Image classification using RandomForest: An example in Python using CIFAR10 Dataset

Image classification using RandomForest: An example in Python using CIFAR10 Dataset     Image classification is a task of assigning a label to an image based on its visual content. It is a fundamental problem in the field of computer vision and has many practical applications, such as self-driving cars and image search engines. One …

Image classification using LightGBM: An example in Python using CIFAR10 Dataset

Image classification using LightGBM: An example in Python using CIFAR10 Dataset     Image classification is a task of assigning a label to an image based on its visual content. It is a fundamental problem in the field of computer vision and has many practical applications, such as self-driving cars and image search engines. One …