Remove 2019 Remove Decision Trees Remove Deep Learning
article thumbnail

Top Stories, Aug 19-25: Top Handy SQL Features for Data Scientists; Nothing but NumPy: Understanding & Creating Neural Networks with Computational Graphs from Scratch

KDnuggets

Also: Deep Learning for NLP: Creating a Chatbot with Keras!; Understanding Decision Trees for Classification in Python; How to Become More Marketable as a Data Scientist; Is Kaggle Learn a Faster Data Science Education?

article thumbnail

Transformer Models: The future of Natural Language Processing

Data Science Dojo

Transformer models are a type of deep learning model that are used for natural language processing (NLP) tasks. They are able to learn long-range dependencies between words in a sentence, which makes them very powerful for tasks such as machine translation, text summarization, and question answering.

professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Transformer Models: The future of Natural Language Processing

Data Science Dojo

Transformer models are a type of deep learning model that are used for natural language processing (NLP) tasks. They are able to learn long-range dependencies between words in a sentence, which makes them very powerful for tasks such as machine translation, text summarization, and question answering.

article thumbnail

Explainability in AI and Machine Learning Systems: An Overview

Heartbeat

Through the explainability of AI systems, it becomes easier to build trust, ensure accountability, and enable humans to comprehend and validate the decisions made by these models. For example, explainability is crucial if a healthcare professional uses a deep learning model for medical diagnoses. Russell, C. & Singh, S. &

article thumbnail

Explainable AI and ChatGPT Detection

Mlearning.ai

One such model could be Neural Prototype Trees [11], a model architecture that makes a decision tree off of “prototypes,” or interpretable representations of patterns in data. 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics. [7] Attention is not not Explanation (2019).

AI 52