Deep Learning- Advanced Natural Language Processing and RNNs

Deep Learning: Advanced Natural Language Processing and RNNs, available at $99.99, has an average rating of 4.63, with 68 lectures, based on 6684 reviews, and has 36312 subscribers.

You will learn about 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 This course is ideal for individuals who are Students in machine learning, deep learning, artificial intelligence, and data science or Professionals in machine learning, deep learning, artificial intelligence, and data science or Anyone interested in state-of-the-art natural language processing It is particularly useful for Students in machine learning, deep learning, artificial intelligence, and data science or Professionals in machine learning, deep learning, artificial intelligence, and data science or Anyone interested in state-of-the-art natural language processing.

Enroll now: Deep Learning: Advanced Natural Language Processing and RNNs

Summary

Title: Deep Learning: Advanced Natural Language Processing and RNNs

Price: $99.99

Average Rating: 4.63

Number of Lectures: 68

Number of Published Lectures: 67

Number of Curriculum Items: 68

Number of Published Curriculum Objects: 67

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

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

Who Should Attend

Students in machine learning, deep learning, artificial intelligence, and data science

Professionals in machine learning, deep learning, artificial intelligence, and data science

Anyone interested in state-of-the-art natural language processing

Target Audiences

Students in machine learning, deep learning, artificial intelligence, and data science

Professionals in machine learning, deep learning, artificial intelligence, and data science

Anyone interested in state-of-the-art natural language processing

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

Course Curriculum

Chapter 1: Welcome

Lecture 1: Introduction

Lecture 2: Outline

Lecture 3: Where to get the code

Lecture 4: How to Succeed in this Course

Chapter 2: Recurrent Neural Networks, Convolutional Neural Networks, and Word Embeddings

Lecture 1: Review Section Introduction

Lecture 2: How to Open Files for Windows Users

Lecture 3: What is a word embedding?

Lecture 4: Using word embeddings

Lecture 5: What is a CNN?

Lecture 6: Where to get the data

Lecture 7: CNN Code (part 1)

Lecture 8: CNN Code (part 2)

Lecture 9: What is an RNN?

Lecture 10: GRUs and LSTMs

Lecture 11: Different Types of RNN Tasks

Lecture 12: A Simple RNN Experiment

Lecture 13: RNN Code

Lecture 14: Review Section Summary

Lecture 15: Suggestion Box

Chapter 3: Bidirectional RNNs

Lecture 1: Bidirectional RNNs Motivation

Lecture 2: Bidirectional RNN Experiment

Lecture 3: Bidirectional RNN Code

Lecture 4: Image Classification with Bidirectional RNNs

Lecture 5: Image Classification Code

Lecture 6: Bidirectional RNNs Section Summary

Chapter 4: Sequence-to-sequence models (Seq2Seq)

Lecture 1: Seq2Seq Theory

Lecture 2: Seq2Seq Applications

Lecture 3: Decoding in Detail and Teacher Forcing

Lecture 4: Poetry Revisited

Lecture 5: Poetry Revisited Code 1

Lecture 6: Poetry Revisited Code 2

Lecture 7: Seq2Seq in Code 1

Lecture 8: Seq2Seq in Code 2

Lecture 9: Seq2Seq Section Summary

Chapter 5: Attention

Lecture 1: Attention Section Introduction

Lecture 2: Attention Theory

Lecture 3: Teacher Forcing

Lecture 4: Helpful Implementation Details

Lecture 5: Attention Code 1

Lecture 6: Attention Code 2

Lecture 7: Visualizing Attention

Lecture 8: Building a Chatbot without any more Code

Lecture 9: Attention Section Summary

Chapter 6: Memory Networks

Lecture 1: Memory Networks Section Introduction

Lecture 2: Memory Networks Theory

Lecture 3: Memory Networks Code 1

Lecture 4: Memory Networks Code 2

Lecture 5: Memory Networks Code 3

Lecture 6: Memory Networks Section Summary

Chapter 7: Keras and Tensorflow 2 Basics

Lecture 1: (Review) Keras Discussion

Lecture 2: (Review) Keras Neural Network in Code

Lecture 3: (Review) Keras Functional API

Lecture 4: (Review) How to easily convert Keras into Tensorflow 2.0 code

Chapter 8: Course Conclusion

Lecture 1: What to Learn Next

Chapter 9: Setting Up Your Environment (FAQ by Student Request)

Lecture 1: Pre-Installation Check

Lecture 2: Anaconda Environment Setup

Lecture 3: How to How to install Numpy, Theano, Tensorflow, etc

Chapter 10: Extra Help With Python Coding for Beginners (FAQ by Student Request)

Lecture 1: How to Code by Yourself (part 1)

Lecture 2: How to Code by Yourself (part 2)

Lecture 3: Proof that using Jupyter Notebook is the same as not using it

Lecture 4: Python 2 vs Python 3

Chapter 11: Effective Learning Strategies for Machine Learning (FAQ by Student Request)

Lecture 1: How to Succeed in this Course (Long Version)

Lecture 2: Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?

Lecture 3: Machine Learning and AI Prerequisite Roadmap (pt 1)

Lecture 4: Machine Learning and AI Prerequisite Roadmap (pt 2)

Chapter 12: Appendix / FAQ Finale

Lecture 1: What is the Appendix?

Lecture 2: BONUS

Instructors

Lazy Programmer Inc.
Artificial intelligence and machine learning engineer

Rating Distribution

1 stars: 31 votes

2 stars: 54 votes

3 stars: 335 votes

4 stars: 2295 votes

5 stars: 3971 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!