The Steel quality control is currently done mainly by human visual inspection. Human inspectors classify the defects according to their cause and origin because the inspection results are used as feedback to correct the manufacturing process. The experience of the inspector is essential, because there are no fixed defect criteria. The inspector's pass/reject decisions seem to be based on the types of defects and their extent, the maximum number of defects per unit of surface area and the total number of defects on the entire inspected strip. In addition, the inspector's knowledge of the customer and the use of the strip have a great impact on the decisions.

Many data scientists fail to fully understand the problems imbalanced datasets cause and the methods to alleviate this. As data scientists we come across many different datasets where there is a clear dominance in some types of data instances (known as majority classes) with other types significantly underrepresented (minority classes). This has significant implications for the practice of data science, where simply training a model on a dataset with this characteristic will likely lead to bias towards the majority classes. For example, if we were focussed on predicting heart disease and had a dataset of 20 people with the disease and 80 without, we could have a case with a model predicting no disease every time and as such achieving a solid accuracy score of 80% and an F1-score of 88%. Despite this well-known problem, there are too many cases where data scientists have ignored this issue and just trained a model without a real understanding of imbalances within the dataset.

This is in continuation with the main article Next Word Prediction using Swiftkey Data . I will discuss in detail the text features and architecture of the LSTM models over the following sections . I have created sequences of different lengths from the tokens of the cleaned corpus maintaining the order of the tokens . I have created sequences of length 2, 4 and 7 . I have encoded the words in the sequences using Keras Tokenizer and splitted the sequences such that only the last word in the sequence is in y and the rest is in x . Thus a sequence of length 2 will have first word in x and the last in y .

Open source refers to something people can modify and share because they are accessible to everyone. You can use the work in new ways, integrate it into a larger project, or find a new work based on the original. Open source promotes the free exchange of ideas within a community to build creative and technological innovations or ideas. It helps you to write cleaner code. That can be of any choice.

Rapidly evolving technologies like Machine Learning, Artificial Intelligence, and Data Science were undoubtedly among the most booming technologies of this decade. The s specifically focusses on Machine Learning which, in general, helped improve productivity across several sectors of the industry by more than 40%. It is a no-brainer that Machine Learning jobs are among the most sought-after jobs in the industry. There are various programming languages, such as JavaScript, Python, and many others, that act as a reputable entry point into the world of Machine Learning, and that brings us to the goal behind this write-up. Through this article, we will try to shed some light on more than 10 of the most popular JavaScript libraries to help you learn Machine Learning.

This PyTorch implementation follow the structure of Neural Style Pt Github Link by Justin Johnson where the network is first build and feature map is captured after the architrcture is build. In the original code Official Code Github Link, the feature map is captured during the build of architecture which cause waist of computation. Also, the loss in different layer back prop by simply adding them up and call loss_total.backward() For more information on how to specify training process, check main.py - get_args()