Deep Learning for Natural Language Processing
Machine learning is everywhere in today's NLP, but by and large machine learning amounts to numerical optimization of weights for human designed representations and features. The goal of deep learning is to explore how computers can take advantage of data to develop features and representations appropriate for complex interpretation tasks. This tutorial aims to cover the basic motivation, ideas, models and learning algorithms in deep learning for natural language processing. Recently, these methods have been shown to perform very well on various NLP tasks such as language modeling, POS tagging, named entity recognition, sentiment analysis and paraphrase detection, among others. The most attractive quality of these techniques is that they can perform well without any external hand-designed resources or time-intensive feature engineering. Despite these advantages, many researchers in NLP are not familiar with these methods. Our focus is on insight and understanding, using graphical illustrations and simple, intuitive derivations.
Видео Deep Learning for Natural Language Processing канала Machine Learning TV
Видео Deep Learning for Natural Language Processing канала Machine Learning TV
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
![Natural Language Processing with Graphs](https://i.ytimg.com/vi/BVMx24dtko0/default.jpg)
![Natural Language Processing (NLP) Tutorial | Data Science Tutorial | Simplilearn](https://i.ytimg.com/vi/6WpnxmmkYys/default.jpg)
![Simple Deep Neural Networks for Text Classification](https://i.ytimg.com/vi/wNBaNhvL4pg/default.jpg)
![Yoshua Bengio: Deep Learning Cognition | Full Keynote - AI in 2020 & Beyond](https://i.ytimg.com/vi/GibjI5FoZsE/default.jpg)
![Introduction to Deep Learning, Keras, and TensorFlow](https://i.ytimg.com/vi/URERdVb-lpg/default.jpg)
![How A.I. Traders Will Dominate Hedge Fund Industry | Marshall Chang | TEDxBeaconStreetSalon](https://i.ytimg.com/vi/lzaBbQKUtAA/default.jpg)
![Deep Learning Model For Disease Named Entity Recognition - Sadid Hasan, Senior Director of AI](https://i.ytimg.com/vi/Z8DsyWPCh0s/default.jpg)
![Natural Language Processing](https://i.ytimg.com/vi/bDxFvr1gpSU/default.jpg)
![Natural Language Processing In 10 Minutes | NLP Tutorial For Beginners | NLP Training | Edureka](https://i.ytimg.com/vi/5ctbvkAMQO4/default.jpg)
![Sujit Pal: Building Named Entity Recognition Models Efficiently Using NERDS | PyData LA 2019](https://i.ytimg.com/vi/ilzFiK0nAh8/default.jpg)
![Graph Representation Learning (Stanford university)](https://i.ytimg.com/vi/YrhBZUtgG4E/default.jpg)
![MIT 6.S191 (2020): Recurrent Neural Networks](https://i.ytimg.com/vi/SEnXr6v2ifU/default.jpg)
![Steve Kaufmann: My Method for Learning Languages from Scratch](https://i.ytimg.com/vi/mXqFD2bWHxU/default.jpg)
![Energy-Efficient Deep Learning: Challenges and Opportunities](https://i.ytimg.com/vi/8Qa0E1jdkrE/default.jpg)
![NLP on legal contracts - Uri Goren - PyCon Israel 2019](https://i.ytimg.com/vi/7gvcNjBm6yI/default.jpg)
![Deep Learning Crash Course for Beginners](https://i.ytimg.com/vi/VyWAvY2CF9c/default.jpg)
![Natural Language Processing For Healthcare - Amir Tahmasebi, Director of ML & AI at CODAMETRIX.](https://i.ytimg.com/vi/EzDgw4_gCVU/default.jpg)
![Graph Node Embedding Algorithms (Stanford - Fall 2019)](https://i.ytimg.com/vi/7JELX6DiUxQ/default.jpg)
![Natural Language Search with Knowledge Graphs - Trey Grainger, Lucidworks](https://i.ytimg.com/vi/5noi2VM9F-g/default.jpg)
![IoT and Machine Learning - Changing the Future | Dr. Dennis Ong | TEDxOhioStateUniversity](https://i.ytimg.com/vi/mlE03Fj2T9s/default.jpg)