NLP Certification- BERT, GPTs to Multilingual Implementation
NLP Certification- BERT, GPTs to Multilingual Implementation, SuperGlue & A-Z (50 + coding solutions) for Text Classification, Language Translation, Sentiment Analysis & Lingobots.
This course introduces you to the fundamentals of Transformers in NLP. The topics include are;
1. Recurrent Neural Networks & LSTM
2. Bi-Directional Encoder Representation from Transformers.
3. Masked Language Modelling.
4. Next Sentence Prediction using Transformers.
5. Generative Pre-trained Transformers and their implementation in RASA and SpiCy.
6. Complete Code for Online Fraud Detection System.
7. Complete Code for Text Classification.
8. Complete Code for Language Translation System.
9. Complete Code for Movie Recommender System.
10. Complete Code for Speech to Text Conversion using GPT-2.
11. Complete Code for Chatbot using GPT3.
12. Complete Code for Text Summary System using GPT3.
13. Automated Essay Scoring using Transformer Models.
14. Sentiment Analysis using Pre-trained Transformers.
15. Training and Testing a GPT- 2 for Novel Writing.
16. Game Design using AlphaGo and Transformers.
17. 50+ NLP coding exercises along with complete solutions to complete this certification.
Transformers (formerly known as PyTorch-transformers and pytorch-pretrained-bert) provide thousands of pre-trained models to perform tasks on different modalities such as text, vision, and audio.
These models can be applied on:
- Text, for tasks like text classification, information extraction, question answering, summarization, translation, text generation, in over 100 languages.
- Images, for tasks like image classification, object detection, and segmentation.
- Audio, for tasks like speech recognition and audio classification.
Transformer Models are great with Sequential Data and are Pre-trained which makes them versatile and capable. It allows further to Gain Out-of-the-Box Functionality. Transformer models enable you to take a large-scale LM (language model) trained on a massive amount of text (the complete works of Shakespeare), then update the model for a specific conceptual task, far beyond mere “reading,” such as sentiment analysis and even predictive analysis.