Master BERT: The Breakthrough NLP Algorithm
Course Overview:
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Master BERT: The Breakthrough NLP Algorithm
Course Overview:
Master BERT: The Breakthrough NLP Algorithm
Course Overview:
Welcome to the ultimate guide to BERT. This comprehensive course is designed to take you on a journey from the basics to mastery of BERT (Bidirectional Encoder Representations from Transformers), a state-of-the-art algorithm transforming the field of natural language processing (NLP).
Why Choose This Course?
Accessible for Everyone: Whether you're a seasoned data scientist or a newcomer to NLP, this course is crafted to be inclusive and comprehensive. We begin with the origins and history of BERT, carefully explaining each concept so that anyone can follow along. By the end of the course, you'll have a solid grasp of BERT, regardless of your starting point.
Revolutionary and Versatile: BERT has fundamentally changed how we approach NLP tasks by eliminating the need for traditional models like RNNs and CNNs. Instead, BERT uses transformers to provide a more intuitive and effective way to process language. You'll learn how to apply BERT to a wide range of NLP tasks, making your projects more powerful and efficient.
Practical: We prioritize practicality and usability in this course. Using TensorFlow 2.0 and Google Colab, you'll avoid common issues with local machine setups and software compatibility. These tools ensure that you are learning with the most current and advanced technologies available. You'll gain hands-on experience with real-world applications, reinforcing your learning and giving you the confidence to apply BERT in your own projects.
Hands-On Learning: Our course includes numerous practical exercises and projects to help you apply what you’ve learned. You'll work through real-world scenarios and datasets, allowing you to see firsthand how BERT can be used to solve complex NLP problems. This hands-on approach ensures that you're not just learning theory but also gaining the practical skills needed to implement BERT effectively.
Enroll Now:
If you're ready to dive into the world of BERT and revolutionize your approach to natural language processing, this course is for you. Enroll now and start your journey towards mastering one of the most powerful tools in NLP today.
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What's inside
Learning objectives
- Understand the history about bert and why it changed nlp more than any algorithm in the recent years
- Understand how bert is different from other standard algorithm and is closer to how humans process languages
- Use the tokenizing tools provided with bert to preprocess text data efficiently
- Use the bert layer as a embedding to plug it to your own nlp model
- Use bert as a pre-trained model and then fine tune it to get the most out of it
- Explore the github project from the google research team to get the tools we need
- Get models available on tensorflow hub, the platform where you can get already trained models
- Clean text data
- Create datasets for ai from those data
- Use google colab and tensorflow 2.0 for your ai implementations
- Create customs layers and models in tf 2.0 for specific nlp tasks
- Show more
- Show less
- Understand the history about bert and why it changed nlp more than any algorithm in the recent years
- Understand how bert is different from other standard algorithm and is closer to how humans process languages
- Use the tokenizing tools provided with bert to preprocess text data efficiently
- Use the bert layer as a embedding to plug it to your own nlp model
- Use bert as a pre-trained model and then fine tune it to get the most out of it
- Explore the github project from the google research team to get the tools we need
- Get models available on tensorflow hub, the platform where you can get already trained models
- Clean text data
- Create datasets for ai from those data
- Use google colab and tensorflow 2.0 for your ai implementations
- Create customs layers and models in tf 2.0 for specific nlp tasks
- Show more
- Show less
Syllabus
Introduction
Welcome to the course
Course curriculum, Colab toolkit and data links
EXTRA: Learning Path
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Syllabus
Good to know
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Activities
Learn About BERT Basics
Show steps
Start the course off strong with a firm grasp on the basics of what BERT is and how it's used.
Browse courses on
Natural Language Processing
Show steps
-
Watch Introduction to BERT video on course landing page.
-
Review course notes on BERT history and architecture.
-
Complete the BERT Intuition quiz.
BERT Tokenization Exercise
Show steps
Reinforce your understanding of how BERT tokenizes text data by completing this exercise.
Browse courses on
Natural Language Processing
Show steps
-
Load the necessary libraries and import the text data.
-
Create a BERT tokenizer using the appropriate function.
-
Tokenize the text data using the BERT tokenizer.
-
Print the tokenized text data to verify the results.
BERT As An Embedder Demo
Show steps
Solidify your knowledge of using BERT as an embedder by creating a demo that showcases its capabilities.
Browse courses on
Natural Language Processing
Show steps
-
Load the necessary libraries and import the required data.
-
Create a BERT model and load the pre-trained weights.
-
Use the BERT model to generate embeddings for the input text.
-
Visualize the embeddings using a dimensionality reduction technique.
Two other activities
Expand to see all activities and additional details
Show all five activities
BERT Resource Collection
Show steps
Facilitate future learning by compiling a collection of valuable BERT resources, tools, and tutorials.
Browse courses on
Natural Language Processing
Show steps
-
Search for and identify relevant BERT resources.
-
Organize the resources into a structured format.
-
Document the resources and provide brief descriptions.
Fine-Tuning BERT for Question Answering
Show steps
Challenge yourself by fine-tuning a pre-trained BERT model for question answering tasks, deepening your understanding of its capabilities.
Browse courses on
Question Answering
Show steps
-
Load the necessary libraries and import the required data.
-
Load a pre-trained BERT model and fine-tune it for question answering.
-
Train the model on the question answering dataset.
-
Evaluate the model's performance on a held-out dataset.
Learn About BERT Basics
Show steps
Start the course off strong with a firm grasp on the basics of what BERT is and how it's used.
Browse courses on
Natural Language Processing
Show steps
- Watch Introduction to BERT video on course landing page.
- Review course notes on BERT history and architecture.
- Complete the BERT Intuition quiz.
BERT Tokenization Exercise
Show steps
Reinforce your understanding of how BERT tokenizes text data by completing this exercise.
Browse courses on
Natural Language Processing
Show steps
- Load the necessary libraries and import the text data.
- Create a BERT tokenizer using the appropriate function.
- Tokenize the text data using the BERT tokenizer.
- Print the tokenized text data to verify the results.
BERT As An Embedder Demo
Show steps
Solidify your knowledge of using BERT as an embedder by creating a demo that showcases its capabilities.
Browse courses on
Natural Language Processing
Show steps
- Load the necessary libraries and import the required data.
- Create a BERT model and load the pre-trained weights.
- Use the BERT model to generate embeddings for the input text.
- Visualize the embeddings using a dimensionality reduction technique.
BERT Resource Collection
Show steps
Facilitate future learning by compiling a collection of valuable BERT resources, tools, and tutorials.
Browse courses on
Natural Language Processing
Show steps
- Search for and identify relevant BERT resources.
- Organize the resources into a structured format.
- Document the resources and provide brief descriptions.
Fine-Tuning BERT for Question Answering
Show steps
Challenge yourself by fine-tuning a pre-trained BERT model for question answering tasks, deepening your understanding of its capabilities.
Browse courses on
Question Answering
Show steps
- Load the necessary libraries and import the required data.
- Load a pre-trained BERT model and fine-tune it for question answering.
- Train the model on the question answering dataset.
- Evaluate the model's performance on a held-out dataset.
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