Author Archives: Vijaysinh Lendave - Page 18
The most used validation technique is K-Fold Cross-validation which involves splitting the training dataset into k folds. The first k-1 folds are used for training, and the remaining fold is held for testing, which is repeated for K-folds. A total of K folds are fit and evaluated, and the mean accuracy for all these folds is returned.
The post Hands-On Tutorial on Performance Measure of Stratified K-Fold Cross-Validation appeared first on Analytics India Magazine.
We are learning statistics because we can; observe the information properly, draw the conclusion from the large volume of the dataset, make reliable forecasts about business activity and improve the business process. To do all kinds of these analyses, statistics are used. Further, it is classified into two types: Descriptive and Inferential statistics.
The post Complete Guide To Descriptive Statistics in Python for Beginners appeared first on Analytics India Magazine.
We are learning statistics because we can; observe the information properly, draw the conclusion from the large volume of the dataset, make reliable forecasts about business activity and improve the business process. To do all kinds of these analyses, statistics are used. Further, it is classified into two types: Descriptive and Inferential statistics.
The post Complete Guide To Descriptive Statistics in Python for Beginners appeared first on Analytics India Magazine.
We are learning statistics because we can; observe the information properly, draw the conclusion from the large volume of the dataset, make reliable forecasts about business activity and improve the business process. To do all kinds of these analyses, statistics are used. Further, it is classified into two types: Descriptive and Inferential statistics.
The post Complete Guide To Descriptive Statistics in Python for Beginners appeared first on Analytics India Magazine.
Distributed training in TensorFlow is built around data parallelism, where we can replicate the same model architecture on multiple devices and run different slices of input data on them. Here the device is nothing but a unit of CPU + GPU or separate units of GPUs and TPUs. This method follows like; our entire data is divided into equal numbers of slices. These slices are decided based on available devices to train; following each slice, there is a model to train on that slice.
The post Hands-On Guide To Custom Training With Tensorflow Strategy appeared first on Analytics India Magazine.
Many machine learning models, including neural networks, consistently misclassify the adversarial examples. Adversarial examples are nothing but specialised inputs created to confuse neural networks, ultimately resulting in misclassification of the result. These notorious inputs are almost the same as the original image to human eyes but cause a neural network to fail to identify the image’s content.
The post How To Confuse a Neural Network Using Fast Gradient Sign Method? appeared first on Analytics India Magazine.
By the facts, a very large amount of unstructured data represents huge under-exploited opportunities. For example, if you look at our daily communication, you get what the person wants to say or convey and easily interpret their attitude towards you. So, in short, unstructured data is complex, but with the right tools and proper techniques, we can easily get those insights.
The post Hands-On Guide To Librosa For Handling Audio Files appeared first on Analytics India Magazine.
An increase in data analytics and data integration has made way for more specialized visual analytical tools. Typically files like excel spreadsheets are very good with analytics and visualization, but it has limitations like it can not handle big data, which is our main concern. On the other hand, specialised software leverages easy operation on both static and dynamic data, computational speed, self-service function, and interactive visualization facilitate users to pull up a report or dashboard or storyline and freely deep dive to granular levels of information.
The post How To Create Interactive Public Dashboards And Storylines In Tableau? appeared first on Analytics India Magazine.
Image extrapolation is such a task in computer vision that aims to fill the surrounding region of a sub-image, e.g. completing the object appearing in the image or predicting the unseen view from the scene picture. This task is extremely challenging since the extrapolated image must be realistic with reasonable and meaningful context. Moreover, the extrapolated region should be consistent in structure and texture with the original sub-image.
The post Hands-On Guide To Image Extrapolation With Boundless-GAN appeared first on Analytics India Magazine.
Due to the explosion of the internet and the existence of several multicultural communities, one of the major challenges faced by this system is multilingual. In a multilingual scenario, it is expected that the QA system will be able to do: answer questions formulated in several languages and look for answers in several collections in different languages. There are two kinds of recognizable QA systems that manage information in different languages, i.e. cross-lingual QA system and a second multilingual QA system. The first one addresses the situation where questions are formulated in different languages from a single document. The second one performs a search over two or more document collections in different languages.
The post Guide To Question Answer Retrieval With Multilingual Universal Sentence Encoder appeared first on Analytics India Magazine.