Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.
In this course we are going to look at NLP (natural language processing) with deep learning.
Previously, you learned about some of the basics, like how many NLP problems are just regular machine learning and data science problems in disguise, and simple, practical methods like bag-of-words and term-document matrices.
Read more
Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.
In this course we are going to look at NLP (natural language processing) with deep learning.
Previously, you learned about some of the basics, like how many NLP problems are just regular machine learning and data science problems in disguise, and simple, practical methods like bag-of-words and term-document matrices.
Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.
In this course we are going to look at NLP (natural language processing) with deep learning.
Previously, you learned about some of the basics, like how many NLP problems are just regular machine learning and data science problems in disguise, and simple, practical methods like bag-of-words and term-document matrices.
These allowed us to do some pretty cool things, like detect spam emails, write poetry, spin articles, and group together similar words.
In this course I’m going to show you how to do even more awesome things. We’ll learn not just 1, but 4 new architectures in this course.
First up is word2vec.
In this course, I’m going to show you exactly how word2vec works, from theory to implementation, and you’ll see that it’s merely the application of skills you already know.
Word2vec is interesting because it magically maps words to a vector space where you can find analogies, like:
king - man = queen - woman
France - Paris = England - London
December - Novemeber = July - June
For those beginners who find algorithms tough and just want to use a library, we will demonstrate the use of the Gensim library to obtain pre-trained word vectors, compute similarities and analogies, and apply those word vectors to build text classifiers.
We are also going to look at the GloVe method, which also finds word vectors, but uses a technique called matrix factorization, which is a popular algorithm for recommender systems.
Amazingly, the word vectors produced by GLoVe are just as good as the ones produced by word2vec, and it’s way easier to train.
We will also look at some classical NLP problems, like parts-of-speech tagging and named entity recognition, and use recurrent neural networks to solve them. You’ll see that just about any problem can be solved using neural networks, but you’ll also learn the dangers of having too much complexity.
Lastly, you’ll learn about recursive neural networks, which finally help us solve the problem of negation in sentiment analysis. Recursive neural networks exploit the fact that sentences have a tree structure, and we can finally get away from naively using bag-of-words.
All of the materials required for this course can be downloaded and installed for FREE. We will do most of our work in Numpy, Matplotlib, and Theano. I am always available to answer your questions and help you along your data science journey.
This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.
See you in class.
"If you can't implement it, you don't understand it"
Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".
My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch
Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?
After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...
Suggested Prerequisites:
calculus (taking derivatives)
matrix addition, multiplication
probability (conditional and joint distributions)
Python coding: if/else, loops, lists, dicts, sets
Numpy coding: matrix and vector operations, loading a CSV file
neural networks and backpropagation, be able to derive and code gradient descent algorithms on your own
Can write a feedforward neural network in Theano or TensorFlow
Can write a recurrent neural network / LSTM / GRU in Theano or TensorFlow from basic primitives, especially the scan function
Helpful to have experience with tree algorithms
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:
Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)
UNIQUE FEATURES
Every line of code explained in detail - email me any time if you disagree
No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch
Not afraid of university-level math - get important details about algorithms that other courses leave out
Here's a deal for you
UDEAFNULP2024
What's inside
Learning objectives
- Understand and implement word2vec
- Understand the cbow method in word2vec
- Understand the skip-gram method in word2vec
- Understand the negative sampling optimization in word2vec
- Understand and implement glove using gradient descent and alternating least squares
- Use recurrent neural networks for parts-of-speech tagging
- Use recurrent neural networks for named entity recognition
- Understand and implement recursive neural networks for sentiment analysis
- Understand and implement recursive neural tensor networks for sentiment analysis
- Use gensim to obtain pretrained word vectors and compute similarities and analogies
- Understand important foundations for openai chatgpt, gpt-4, dall-e, midjourney, and stable diffusion
- Show more
- Show less
- Understand and implement word2vec
- Understand the cbow method in word2vec
- Understand the skip-gram method in word2vec
- Understand the negative sampling optimization in word2vec
- Understand and implement glove using gradient descent and alternating least squares
- Use recurrent neural networks for parts-of-speech tagging
- Use recurrent neural networks for named entity recognition
- Understand and implement recursive neural networks for sentiment analysis
- Understand and implement recursive neural tensor networks for sentiment analysis
- Use gensim to obtain pretrained word vectors and compute similarities and analogies
- Understand important foundations for openai chatgpt, gpt-4, dall-e, midjourney, and stable diffusion
- Show more
- Show less
Syllabus
Outline, Review, and Logistical Things
Introduction, Outline, and Review
How to Succeed in this Course
Where to get the code / data for this course
Read more
Syllabus
Good to know
Save this course
Save
Reviews summary
Top-rated nlp course
Lots of great exercises
Top-notch instructor
Excellent course in advanced NLP.
Activities
Compile a list of resources on NLP for future reference
Show steps
Organize and gather resources to support your NLP learning journey.
Show steps
-
Create a document or spreadsheet to store your resources.
-
Categorize resources based on topic, such as tutorials, research papers, and datasets.
-
Continuously add to your list as you discover new resources.
Read 'Deep Learning' by Goodfellow, Bengio, and Courville
Show steps
Gain a comprehensive understanding of deep learning concepts and techniques.
View
Deep Learning
on Amazon
Show steps
-
Read the book thoroughly, taking notes and highlighting important concepts.
-
Work through the exercises and examples provided in the book to reinforce your understanding.
-
Discuss the concepts with peers or mentors to deepen your comprehension.
Join a study group with peers to discuss NLP concepts
Show steps
Collaborate with peers to enhance your understanding of NLP.
Show steps
-
Find or create a study group with other students who are taking the same or similar NLP courses.
-
Meet regularly to discuss lecture material, work on assignments together, and ask questions.
-
Take turns presenting concepts to the group to reinforce your understanding.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve NLP problems using recurrent neural networks
Show steps
Reinforce your understanding of recurrent neural networks by solving NLP problems.
Show steps
-
Identify NLP problems that can be solved using recurrent neural networks, such as parts-of-speech tagging or named entity recognition.
-
Implement recurrent neural network models in your preferred programming language.
-
Train and evaluate your models on real-world datasets.
Attend an NLP workshop on advanced techniques
Show steps
Gain exposure to cutting-edge NLP techniques and applications.
Show steps
-
Search for NLP workshops or conferences that focus on advanced techniques.
-
Identify workshops that align with your interests and learning goals.
-
Register for the workshop and actively participate in the sessions.
Develop a chatbot using NLP techniques
Show steps
Apply your NLP knowledge to build a practical application.
Show steps
-
Design the chatbot's functionality and user interface.
-
Implement natural language processing techniques for understanding user input.
-
Develop a knowledge base or retrieval system to provide relevant responses.
-
Train and evaluate the chatbot using real-world data.
-
Deploy the chatbot and gather feedback for further improvement.
Follow a TensorFlow tutorial on word embeddings
Show steps
Deepen your understanding of word embeddings by following a guided tutorial on TensorFlow.
Show steps
-
Find a comprehensive TensorFlow tutorial on word embeddings.
-
Follow the tutorial step-by-step, implementing the code yourself.
-
Experiment with different parameters and datasets to observe the impact on embedding quality.
Build a custom word embedding model for a specific domain
Show steps
Develop your skills in creating and customizing word embedding models.
Show steps
-
Identify a specific domain for which you want to create a custom word embedding model, such as healthcare or finance.
-
Collect a large corpus of text data from the chosen domain.
-
Preprocess the text data, including tokenization, stop word removal, and lemmatization.
-
Train a word embedding model using the preprocessed data, experimenting with different parameters and algorithms.
-
Evaluate the performance of your model on a downstream NLP task, such as text classification or question answering.
Compile a list of resources on NLP for future reference
Show steps
Organize and gather resources to support your NLP learning journey.
Show steps
- Create a document or spreadsheet to store your resources.
- Categorize resources based on topic, such as tutorials, research papers, and datasets.
- Continuously add to your list as you discover new resources.
Read 'Deep Learning' by Goodfellow, Bengio, and Courville
Show steps
Gain a comprehensive understanding of deep learning concepts and techniques.
View
Deep Learning
on Amazon
Show steps
- Read the book thoroughly, taking notes and highlighting important concepts.
- Work through the exercises and examples provided in the book to reinforce your understanding.
- Discuss the concepts with peers or mentors to deepen your comprehension.
Join a study group with peers to discuss NLP concepts
Show steps
Collaborate with peers to enhance your understanding of NLP.
Show steps
- Find or create a study group with other students who are taking the same or similar NLP courses.
- Meet regularly to discuss lecture material, work on assignments together, and ask questions.
- Take turns presenting concepts to the group to reinforce your understanding.
Solve NLP problems using recurrent neural networks
Show steps
Reinforce your understanding of recurrent neural networks by solving NLP problems.
Show steps
- Identify NLP problems that can be solved using recurrent neural networks, such as parts-of-speech tagging or named entity recognition.
- Implement recurrent neural network models in your preferred programming language.
- Train and evaluate your models on real-world datasets.
Attend an NLP workshop on advanced techniques
Show steps
Gain exposure to cutting-edge NLP techniques and applications.
Show steps
- Search for NLP workshops or conferences that focus on advanced techniques.
- Identify workshops that align with your interests and learning goals.
- Register for the workshop and actively participate in the sessions.
Develop a chatbot using NLP techniques
Show steps
Apply your NLP knowledge to build a practical application.
Show steps
- Design the chatbot's functionality and user interface.
- Implement natural language processing techniques for understanding user input.
- Develop a knowledge base or retrieval system to provide relevant responses.
- Train and evaluate the chatbot using real-world data.
- Deploy the chatbot and gather feedback for further improvement.
Follow a TensorFlow tutorial on word embeddings
Show steps
Deepen your understanding of word embeddings by following a guided tutorial on TensorFlow.
Show steps
- Find a comprehensive TensorFlow tutorial on word embeddings.
- Follow the tutorial step-by-step, implementing the code yourself.
- Experiment with different parameters and datasets to observe the impact on embedding quality.
Build a custom word embedding model for a specific domain
Show steps
Develop your skills in creating and customizing word embedding models.
Show steps
- Identify a specific domain for which you want to create a custom word embedding model, such as healthcare or finance.
- Collect a large corpus of text data from the chosen domain.
- Preprocess the text data, including tokenization, stop word removal, and lemmatization.
- Train a word embedding model using the preprocessed data, experimenting with different parameters and algorithms.
- Evaluate the performance of your model on a downstream NLP task, such as text classification or question answering.
Career center
Data Scientist
Natural Language Processing Engineer
Machine Learning Engineer
Software Engineer
Data Analyst
Business Analyst
Marketing Manager
Sales Manager
Product Manager
Technical Writer
Customer Success Manager
UX Designer
Social Media Manager
Content Writer
Community Manager
Reading list
Save
Save
Share
Similar courses
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
