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.
It’s hard to believe it's been been over a year since I released my first course on Deep Learning with NLP (natural language processing).
A lot of cool stuff has happened since then, and I've been deep in the trenches learning, researching, and accumulating the best and most useful ideas to bring them back to you.
So what is this course all about, and how have things changed since then?
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.
It’s hard to believe it's been been over a year since I released my first course on Deep Learning with NLP (natural language processing).
A lot of cool stuff has happened since then, and I've been deep in the trenches learning, researching, and accumulating the best and most useful ideas to bring them back to you.
So what is this course all about, and how have things changed since then?
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.
It’s hard to believe it's been been over a year since I released my first course on Deep Learning with NLP (natural language processing).
A lot of cool stuff has happened since then, and I've been deep in the trenches learning, researching, and accumulating the best and most useful ideas to bring them back to you.
So what is this course all about, and how have things changed since then?
In previous courses, you learned about some of the fundamental building blocks of Deep NLP. We looked at RNNs (recurrent neural networks), CNNs (convolutional neural networks), and word embedding algorithms such as word2vec and GloVe.
This course takes you to a higher systems level of thinking.
Since you know how these things work, it’s time to build systems using these components.
At the end of this course, you'll be able to build applications for problems like:
text classification (examples are sentiment analysis and spam detection)
neural machine translation
question answering
We'll take a brief look chatbots and as you’ll learn in this course, this problem is actually no different from machine translation and question answering.
To solve these problems, we’re going to look at some advanced Deep NLP techniques, such as:
bidirectional RNNs
seq2seq (sequence-to-sequence)
attention
memory networks
All of the materials of this course can be downloaded and installed for FREE. We will do most of our work in Python libraries such as Keras, Numpy, Tensorflow, and Matpotlib to make things super easy and focus on the high-level concepts. 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:
Decent Python coding skills
Understand RNNs, CNNs, and word embeddings
Know how to build, train, and evaluate a neural network in Keras
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
- Build a text classification system (can be used for spam detection, sentiment analysis, and similar problems)
- Build a neural machine translation system (can also be used for chatbots and question answering)
- Build a sequence-to-sequence (seq2seq) model
- Build an attention model
- Build a memory network (for question answering based on stories)
- Understand important foundations for openai chatgpt, gpt-4, dall-e, midjourney, and stable diffusion
- Build a text classification system (can be used for spam detection, sentiment analysis, and similar problems)
- Build a neural machine translation system (can also be used for chatbots and question answering)
- Build a sequence-to-sequence (seq2seq) model
- Build an attention model
- Build a memory network (for question answering based on stories)
- Understand important foundations for openai chatgpt, gpt-4, dall-e, midjourney, and stable diffusion
Syllabus
Welcome
Introduction
Outline
Where to get the code
Read more
Syllabus
Good to know
Save this course
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Reviews summary
Comprehensive deep learning in nlp
Instructor's expertise in NLP
In-depth knowledge of NLP models
Activities
Review core machine learning concepts
Show steps
Review the fundamentals of machine learning, such as supervised and unsupervised learning, feature engineering, and model evaluation. This will strengthen your foundation for the advanced techniques covered in the course.
Browse courses on
Machine Learning Basics
Show steps
-
Read through your notes or textbooks on machine learning concepts.
-
Complete practice problems or online quizzes to test your understanding.
Explore open-source implementations of AI models
Show steps
Familiarize yourself with the inner workings of AI models by studying open-source implementations. This will provide insights into the practical applications and limitations of these technologies.
Browse courses on
OpenAI ChatGPT
Show steps
-
Identify open-source repositories for AI models of interest.
-
Read through the documentation and code to understand the model architecture and training process.
-
Experiment with different model parameters and datasets to observe their impact on performance.
Build a binary text classifier
Show steps
Implement a binary text classifier from scratch using techniques like bag-of-words or TF-IDF. This will provide hands-on experience with the core concepts of text classification.
Browse courses on
Text Classification
Show steps
-
Gather a dataset of labeled text data.
-
Preprocess the text data by removing stop words and stemming.
-
Create a bag-of-words or TF-IDF representation of the text.
-
Train a logistic regression or SVM classifier on the data.
-
Evaluate the performance of your classifier using metrics like accuracy and F1-score.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Create a visualization of a neural network architecture
Show steps
Enhance your understanding of neural network architectures by creating a visual representation. This will help you visualize the flow of data and operations within the network.
Browse courses on
Neural Network Architecture
Show steps
-
Choose a neural network architecture, such as CNN or RNN.
-
Use a tool or library to create a diagram of the architecture.
-
Annotate the diagram with details about the layers, activation functions, and connections.
Practice coding deep learning models in Python
Show steps
Enhance your proficiency in coding deep learning models by practicing regularly. This will build your confidence and ability to implement complex models independently.
Browse courses on
Python Programming
Show steps
-
Set aside dedicated time for coding practice.
-
Find online coding challenges or exercises related to deep learning.
-
Implement code for different deep learning architectures and algorithms.
Attend a workshop on deep learning applications
Show steps
Gain practical experience in applying deep learning techniques by attending a workshop. This will provide you with hands-on guidance and exposure to real-world use cases.
Browse courses on
Deep Learning Applications
Show steps
-
Research and identify relevant workshops in your area or online.
-
Register for the workshop and prepare any necessary materials.
-
Actively participate in the workshop, ask questions, and take notes.
Develop a neural machine translation system
Show steps
Build a neural machine translation system using an encoder-decoder architecture. This project will challenge you to apply the advanced techniques covered in the course to a real-world problem.
Browse courses on
Neural Machine Translation
Show steps
-
Choose a pair of languages for translation.
-
Gather a parallel corpus of translated text.
-
Preprocess the text data by tokenizing and normalizing.
-
Create an encoder and decoder network using Keras or Tensorflow.
-
Train the neural machine translation system using the parallel corpus.
-
Evaluate the performance of your system using metrics like BLEU score.
Participate in a machine learning competition
Show steps
Challenge yourself and test your skills by participating in a machine learning competition. This will provide you with valuable experience in solving real-world problems and collaborating with others.
Show steps
-
Identify a machine learning competition that aligns with your interests.
-
Explore the competition dataset and familiarize yourself with the problem statement.
-
Develop and implement your machine learning solution.
-
Submit your solution and analyze your results.
Review core machine learning concepts
Show steps
Review the fundamentals of machine learning, such as supervised and unsupervised learning, feature engineering, and model evaluation. This will strengthen your foundation for the advanced techniques covered in the course.
Browse courses on
Machine Learning Basics
Show steps
- Read through your notes or textbooks on machine learning concepts.
- Complete practice problems or online quizzes to test your understanding.
Explore open-source implementations of AI models
Show steps
Familiarize yourself with the inner workings of AI models by studying open-source implementations. This will provide insights into the practical applications and limitations of these technologies.
Browse courses on
OpenAI ChatGPT
Show steps
- Identify open-source repositories for AI models of interest.
- Read through the documentation and code to understand the model architecture and training process.
- Experiment with different model parameters and datasets to observe their impact on performance.
Build a binary text classifier
Show steps
Implement a binary text classifier from scratch using techniques like bag-of-words or TF-IDF. This will provide hands-on experience with the core concepts of text classification.
Browse courses on
Text Classification
Show steps
- Gather a dataset of labeled text data.
- Preprocess the text data by removing stop words and stemming.
- Create a bag-of-words or TF-IDF representation of the text.
- Train a logistic regression or SVM classifier on the data.
- Evaluate the performance of your classifier using metrics like accuracy and F1-score.
Create a visualization of a neural network architecture
Show steps
Enhance your understanding of neural network architectures by creating a visual representation. This will help you visualize the flow of data and operations within the network.
Browse courses on
Neural Network Architecture
Show steps
- Choose a neural network architecture, such as CNN or RNN.
- Use a tool or library to create a diagram of the architecture.
- Annotate the diagram with details about the layers, activation functions, and connections.
Practice coding deep learning models in Python
Show steps
Enhance your proficiency in coding deep learning models by practicing regularly. This will build your confidence and ability to implement complex models independently.
Browse courses on
Python Programming
Show steps
- Set aside dedicated time for coding practice.
- Find online coding challenges or exercises related to deep learning.
- Implement code for different deep learning architectures and algorithms.
Attend a workshop on deep learning applications
Show steps
Gain practical experience in applying deep learning techniques by attending a workshop. This will provide you with hands-on guidance and exposure to real-world use cases.
Browse courses on
Deep Learning Applications
Show steps
- Research and identify relevant workshops in your area or online.
- Register for the workshop and prepare any necessary materials.
- Actively participate in the workshop, ask questions, and take notes.
Develop a neural machine translation system
Show steps
Build a neural machine translation system using an encoder-decoder architecture. This project will challenge you to apply the advanced techniques covered in the course to a real-world problem.
Browse courses on
Neural Machine Translation
Show steps
- Choose a pair of languages for translation.
- Gather a parallel corpus of translated text.
- Preprocess the text data by tokenizing and normalizing.
- Create an encoder and decoder network using Keras or Tensorflow.
- Train the neural machine translation system using the parallel corpus.
- Evaluate the performance of your system using metrics like BLEU score.
Participate in a machine learning competition
Show steps
Challenge yourself and test your skills by participating in a machine learning competition. This will provide you with valuable experience in solving real-world problems and collaborating with others.
Show steps
- Identify a machine learning competition that aligns with your interests.
- Explore the competition dataset and familiarize yourself with the problem statement.
- Develop and implement your machine learning solution.
- Submit your solution and analyze your results.
Career center
Natural Language Generation Researcher
Computational Linguist
Information Retrieval Specialist
Text Mining Analyst
Question Answering Researcher
Machine Translation Researcher
Professor
Speech Scientist
Research Scientist
Natural Language Processing Engineer
Machine Learning Engineer
Software Engineer
AI Engineer
Technical Writer
Data Scientist
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