How much data is enough for deep learning?

For most “average” problems, you should have 10,000 – 100,000 examples. For “hard” problems like machine translation, high dimensional data generation, or anything requiring deep learning, you should try to get 100,000 – 1,000,000 examples. Generally, the more dimensions your data has, the more data you need.

Which deep learning model is best for text classification?

The two main deep learning architectures for text classification are Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). The answer by Chiranjibi Sitaula is the most accurate.

Is deep learning good for text classification?

Deep learning methods are proving very good at text classification, achieving state-of-the-art results on a suite of standard academic benchmark problems.

How big should my dataset be?

The Size of a Data Set As a rough rule of thumb, your model should train on at least an order of magnitude more examples than trainable parameters. Simple models on large data sets generally beat fancy models on small data sets.

How much data is required for neural networks?

According to Yaser S. Abu-Mostafa(Professor of Electrical Engineering and Computer Science) to get a proper result you must have data for at-least 10 times the degree of freedom. example for a neural network which has 3 weights you should have 30 data points.

Which classifier is best for text classification?

Linear Support Vector Machine is widely regarded as one of the best text classification algorithms. We achieve a higher accuracy score of 79\% which is 5\% improvement over Naive Bayes.

How do you label data for text classification?

A good approach to label text is defining clear rules of what should receive which label. Once you do a list of rules, be consistent. If you classify profanity as negative, don’t label the other half of the dataset as positive if they contain profanity.

What is Bert ML?

BERT is an open source machine learning framework for natural language processing (NLP). BERT is designed to help computers understand the meaning of ambiguous language in text by using surrounding text to establish context. BERT is different because it is designed to read in both directions at once.

Can we use deep learning for classification?

Deep learning neural networks are an example of an algorithm that natively supports multi-label classification problems. Neural network models for multi-label classification tasks can be easily defined and evaluated using the Keras deep learning library.

What is dataset in deep learning?

A dataset in machine learning is, quite simply, a collection of data pieces that can be treated by a computer as a single unit for analytic and prediction purposes. This means that the data collected should be made uniform and understandable for a machine that doesn’t see data the same way as humans do.

How good is deep learning at text classification?

Deep learning methods are proving very good at text classification, achieving state-of-the-art results on a suite of standard academic benchmark problems. In this post, you will discover some best practices to consider when developing deep learning models for text classification.

What is texttext classification in machine learning?

Text Classification is an example of supervised machine learning task since a labelled dataset containing text documents and their labels is used for train a classifier. An end-to-end text classification pipeline is composed of three main components:

How much data do you need for machine learning?

The amount of data you need depends both on the complexity of your problem and on the complexity of your chosen algorithm. This is a fact, but does not help you if you are at the pointy end of a machine learning project. A common question I get asked is: How much data do I need?

What are the most popular machine learning algorithms for text classification?

Some of the most popular machine learning algorithms for creating text classification models include the naive bayes family of algorithms, support vector machines, and deep learning. Naive Bayes is a family of statistical algorithms we can make use of when doing text classification.