In this article, we will compare the MobileNet and ResNet-50 architectures of the Deep Convolutional Neural Network. First, we will implement these two models in CIFAR-10 classification and then we will evaluate and compare both of their performances and with other transfer learning models in the same task.
In this article, we will discuss how an aspirant can prepare for the Chartered Data ScientistTM. Along with that, we will also recommend some useful study resources for better preparation.
There are a variety of convolutional neural networks and all have their own advantage. With the varying architectures, these models have shown an overwhelming performance
In this article, we will create an ensemble of convolutional neural networks. In this experiment, we will create an ensemble of 10 CNN models and this ensemble will be applied in multi-class prediction of MNIST handwritten digit data set.
In this article, we will learn how to detect objects present in the images. For the detection of objects, we will use the YOLO (You Only Look Once) algorithm and demonstrate this task on a few images. In the result, we will get the image with captioned and highlighted objects with their probability of correct detection.
In this article, we will recognize the fruit where the Convolutional Neural Network will predict the name of the fruit given its image. We will train the network in a supervised manner where images of the fruits will be the input to the network and labels of the fruits will be the output of the network. After successful training, the CNN model will be able to correctly predict the label of the fruit.
In this article, we will employ the AlexNet model provided by the PyTorch as a transfer learning framework with pre-trained ImageNet weights. The network will be trained on the CIFAR-10 dataset for a multi-class image classification problem and finally, we will analyze its classification accuracy when tested on the unseen test images.
In this article, we will discuss the architecture and implementation of AlexNet using Keras library without using transfer learning approach. In the end, we will evaluate the performance of this model in classification.
In this article, we will classify the sign language symbols using the Convolutional Neural Network (CNN). After successful training of the CNN model, the corresponding alphabet of a sign language symbol will be predicted. We will evaluate the classification performance of our model using the non-normalized and normalized confusion matrices. Finally, we will obtain the classification accuracy score of the CNN model in this task.
In this article, we will compare the multi-class classification performance of three popular transfer learning architectures – VGG16, VGG19 and ResNet50. These all three models that we will use are pre-trained on ImageNet dataset. For the experiment, we have taken the CIFAR-10 image dataset that is a popular benchmark in image classification. The performances of all the three models will be compared using the confusion matrices and their average accuracies.
Here we will discuss how a professional certification like Chartered Data Scientist is different from a data science training. The Chartered Data Scientist is a self-study program where the aspirant needs to prepare on their own for the exam and hence, there is no need to attend any training for this.
In this article, we will train the Deep Convolutional Generative Adversarial Network on Fashion MNIST training images in order to generate a new set of fashion apparel images.
In this article, we will discuss fashion apparel recognition using the Convolutional Neural Network (CNN) model. To train the CNN model, we will use the Fashion MNIST dataset. After successful training, the CNN model can predict the name of the class a given apparel item belongs to.
In this article, we will discuss how to detect anomalies present in the temperature data that is available in the time-series format. This data is captured from the sensors of an internal component of a large industrial machine.
Here in this article, we will discuss a hands-on approach to download and analyze twitter data.
In this article, we will implement the multiclass image classification using the VGG-19 Deep Convolutional Network used as a Transfer Learning framework where the VGGNet comes pre-trained on the ImageNet dataset. For the experiment, we will use the CIFAR-10 dataset and classify the image objects into 10 classes. The classification accuracies of the VGG-19 model will be visualized using the non-normalized and normalized confusion matrices.
In this article, we will discuss how to create a library in python to shorten a URL and then we will check this library to shorten multiple URLs in a text file. The shortened URLs will further be written in an output text file. It can work for thousands of URLs in a text file and convert them into their corresponding shortened form without any crash.
Having a designation like Chartered Data Scientist is always worth it. It does not matter at what career level a person is going to opt for it but it definitely would help a data science professional in the long run.
Here we will discuss in detail about the benefits, scope and future of the Chartered Data Scientist.
In this article, we will see a comparison between two time-series forecasting models – ARIMA model and LSTM RNN model. Both of these models are applied in stock price prediction to see the comparison between them.
Here we list down the top premier global professional certifications and designations in data science.
In this article, we will discuss reinforcement learning in Click-Through-Rate (CTR) prediction of web advertisements. We will see the practical implementation of Upper Confidence Bound (UCB), a method of reinforcement learning applied in this task. Using this implementation, one can be able to find the best version of the advertisement from a set of available versions that can get a maximum number of clicks by the visitors on the website.
In this article, we will discuss the association rule learning method with a practical implementation of market basket analysis in python. We will use the Apriori algorithm as an association rule method for market basket analysis.
In this article, we will analyze the reviews of customers about a restaurant and will predict based on these reviews whether the customer has liked the product of the restaurant or not.
In this article, we will show a heterogeneous collection of weak learners to build a hybrid ensemble learning model. Different types of machine learning algorithms are grouped together in this task to work on a classification problem. We will show the performance of individual weak learning models and then the performance of our hybrid ensemble model.
In this article, the CAP curve analysis method has been discussed where it is used to evaluate and compare the performances of four different classifiers in their classification task.
In this article, we will discuss the practical implementation of three dimensionality reduction techniques – Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and
Kernel PCA (KPCA)
In this post, we will discuss a very basic approach to scrap a news article on the web page and summarize it, along with a few more key information. We will also explore how we can save this scraped and summarized result into a text file. This can be saved for future study or for research purposes.
In this article, we will discuss a deep learning technique — deep neural network — that can be deployed for predicting banks’ crisis. This experiment is based on the African economic, banking and systemic crisis data where inflation, currency crisis and bank crisis of 13 African countries between 1860 to 2014 is given. By predicting through a deep learning model, we will see that this model gives a high accuracy in this task.
Identification of customers based on their choices and other behaviors is an important strategy in any organization. This identification may help in approaching customers with
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