Deep Learning: Advanced Natural Language Processing and RNNs from Udemy

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

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

How to Succeed in this Course

Recurrent Neural Networks, Convolutional Neural Networks, and Word Embeddings

Review Section Introduction

How to Open Files for Windows Users

What is a word embedding?

Using word embeddings

What is a CNN?

Where to get the data

CNN Code (part 1)

CNN Code (part 2)

What is an RNN?

GRUs and LSTMs

Different Types of RNN Tasks

A Simple RNN Experiment

RNN Code

Review Section Summary

Suggestion Box

Bidirectional RNNs

Bidirectional RNNs Motivation

Bidirectional RNN Experiment

Bidirectional RNN Code

Image Classification with Bidirectional RNNs

Image Classification Code

Bidirectional RNNs Section Summary

Sequence-to-sequence models (Seq2Seq)

Seq2Seq Theory

Seq2Seq Applications

Decoding in Detail and Teacher Forcing

Poetry Revisited

Poetry Revisited Code 1

Poetry Revisited Code 2

Seq2Seq in Code 1

Seq2Seq in Code 2

Seq2Seq Section Summary

Attention

Attention Section Introduction

Attention Theory

Teacher Forcing

Helpful Implementation Details

Attention Code 1

Attention Code 2

Visualizing Attention

Building a Chatbot without any more Code

Attention Section Summary

Memory Networks

Memory Networks Section Introduction

Memory Networks Theory

Memory Networks Code 1

Memory Networks Code 2

Memory Networks Code 3

Memory Networks Section Summary

Keras and Tensorflow 2 Basics

(Review) Keras Discussion

(Review) Keras Neural Network in Code

(Review) Keras Functional API

(Review) How to easily convert Keras into Tensorflow 2.0 code

Course Conclusion

What to Learn Next

Setting Up Your Environment (FAQ by Student Request)

Pre-Installation Check

Anaconda Environment Setup

How to How to install Numpy, Theano, Tensorflow, etc...

Extra Help With Python Coding for Beginners (FAQ by Student Request)

How to Code by Yourself (part 1)

How to Code by Yourself (part 2)

Proof that using Jupyter Notebook is the same as not using it

Python 2 vs Python 3

Effective Learning Strategies for Machine Learning (FAQ by Student Request)

How to Succeed in this Course (Long Version)

Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?

Machine Learning and AI Prerequisite Roadmap (pt 1)

Machine Learning and AI Prerequisite Roadmap (pt 2)

Appendix / FAQ Finale

What is the Appendix?

BONUS

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Explores the latest developments in AI technologies, including ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion

Develops and builds applications for text classification, neural machine translation, and question answering

Taught by Lazy Programmer Inc., a recognized name in data science education

Requires decent Python coding skills, understanding of RNNs, CNNs, and word embeddings, and experience building, training, and evaluating neural networks in Keras

Advises students to take other courses first as prerequisites, which could extend the time it takes to complete this course

Not suitable for beginners, as it assumes a certain level of knowledge and experience in data science and machine learning concepts

Reviews summary

Comprehensive deep learning in nlp

According to students, this advanced NLP course is highly informative and well-structured, providing learners with a comprehensive understanding of NLP models and techniques. The course is largely praised for its in-depth content and the instructor's expertise.

Instructor's expertise in NLP

In-depth knowledge of NLP models

"I can learn a lot in depth of well-know NLP model."

"This course is really helpful for me."

"I am waiting for these."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Deep Learning: Advanced Natural Language Processing and RNNs with these activities:

Review core machine learning concepts

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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

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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

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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

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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

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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

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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

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Show all eight activities

Create a visualization of a neural network architecture

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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

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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

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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

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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

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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

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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

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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

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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

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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.

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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

Learners who complete Deep Learning: Advanced Natural Language Processing and RNNs will develop knowledge and skills that may be useful to these careers:

Natural Language Generation Researcher

A Natural Language Generation Researcher designs, develops, and maintains natural language generation systems. This course may be useful to a Natural Language Generation Researcher because it provides an introduction to advanced natural language processing and RNNs, which are essential for developing natural language generation systems that can generate text from a given input.

See salaries and explore the career path for Natural Language Generation Researcher

Computational Linguist

A Computational Linguist studies the computational aspects of human language. This course may be useful to a Computational Linguist because it provides an introduction to advanced natural language processing and RNNs, which are essential for developing computational models of human language.

See salaries and explore the career path for Computational Linguist

Information Retrieval Specialist

An Information Retrieval Specialist designs, develops, and maintains information retrieval systems. This course may be useful to an Information Retrieval Specialist because it provides an introduction to advanced natural language processing and RNNs, which are essential for developing information retrieval systems that can find relevant information in a large collection of documents.

See salaries and explore the career path for Information Retrieval Specialist

Text Mining Analyst

A Text Mining Analyst uses text mining techniques to extract insights from text data. This course may be useful to a Text Mining Analyst because it provides an introduction to advanced natural language processing and RNNs, which are essential for developing text mining systems.

See salaries and explore the career path for Text Mining Analyst

Question Answering Researcher

A Question Answering Researcher designs, develops, and maintains question answering systems. This course may be useful to a Question Answering Researcher because it provides an introduction to advanced natural language processing and RNNs, which are essential for developing question answering systems that can answer questions from a large collection of documents.

See salaries and explore the career path for Question Answering Researcher

Machine Translation Researcher

A Machine Translation Researcher designs, develops, and maintains machine translation systems. This course may be useful to a Machine Translation Researcher because it provides an introduction to advanced natural language processing and RNNs, which are essential for developing machine translation systems that can translate text from one language to another.

See salaries and explore the career path for Machine Translation Researcher

Professor

A Professor teaches and conducts research in a variety of fields, including natural language processing. This course may be useful to a Professor because it provides an introduction to advanced natural language processing and RNNs, which are essential for teaching and conducting research in natural language processing.

See salaries and explore the career path for Professor

Speech Scientist

A Speech Scientist studies the science of speech. This course may be useful to a Speech Scientist because it provides an introduction to advanced natural language processing and RNNs, which are essential for developing speech recognition and synthesis systems.

See salaries and explore the career path for Speech Scientist

Research Scientist

A Research Scientist conducts research in a variety of fields, including natural language processing. This course may be useful to a Research Scientist because it provides an introduction to advanced natural language processing and RNNs, which are essential for conducting research in natural language processing.

See salaries and explore the career path for Research Scientist

Natural Language Processing Engineer

A Natural Language Processing Engineer designs, develops, and maintains natural language processing systems. This course may be useful to a Natural Language Processing Engineer because it provides an introduction to advanced natural language processing and RNNs, which are essential for developing natural language processing systems.

See salaries and explore the career path for Natural Language Processing Engineer

Machine Learning Engineer

A Machine Learning Engineer designs, develops, and maintains machine learning systems. This course may be useful to a Machine Learning Engineer because it provides an introduction to advanced natural language processing and RNNs, which are essential for developing machine learning systems that can understand and generate human language.

See salaries and explore the career path for Machine Learning Engineer

Software Engineer

A Software Engineer designs, develops, and maintains software systems. This course may be useful to a Software Engineer because it provides an introduction to advanced natural language processing and RNNs, which are essential for developing software systems that can understand and generate human language.

See salaries and explore the career path for Software Engineer

AI Engineer

An AI Engineer designs, develops, and maintains artificial intelligence systems. This course may be useful to an AI Engineer because it provides an introduction to advanced natural language processing and RNNs, which are essential for developing AI systems that can understand and generate human language.

See salaries and explore the career path for AI Engineer

Technical Writer

A Technical Writer writes technical documentation, such as user manuals and white papers. This course may be useful to a Technical Writer because it provides an introduction to advanced natural language processing and RNNs, which are essential for writing technical documentation that is clear and concise.

See salaries and explore the career path for Technical Writer

Data Scientist

A Data Scientist uses data to solve business problems. This course may be useful to a Data Scientist because it provides an introduction to advanced natural language processing and RNNs, which are essential for developing data-driven solutions to business problems.

See salaries and explore the career path for Data Scientist