Disclosure: when you buy through links on our site, we may earn an affiliate commission.

Deep Learning: Advanced NLP and RNNs

Natural Language Processing with Sequence-to-sequence (seq2seq), Attention, CNNs, RNNs, and Memory Networks!
4.6
4.6/5
(3,610 reviews)
21,151 students
Created by

9.3

CourseMarks Score®

9.9

Freshness

9.2

Feedback

8.1

Content

Platform: Udemy
Video: 8h 14m
Language: English
Next start: On Demand

Top Deep Learning courses:

Detailed Analysis

CourseMarks Score®

9.3 / 10

CourseMarks Score® helps students to find the best classes. We aggregate 18 factors, including freshness, student feedback and content diversity.

Freshness Score

9.9 / 10
This course was last updated on 5/2021.

Course content can become outdated quite quickly. After analysing 71,530 courses, we found that the highest rated courses are updated every year. If a course has not been updated for more than 2 years, you should carefully evaluate the course before enrolling.

Student Feedback

9.2 / 10
We analyzed factors such as the rating (4.6/5) and the ratio between the number of reviews and the number of students, which is a great signal of student commitment.

New courses are hard to evaluate because there are no or just a few student ratings, but Student Feedback Score helps you find great courses even with fewer reviews.

Content Score

8.1 / 10
Video Score: 8.8 / 10
The course includes 8h 14m video content. Courses with more videos usually have a higher average rating. We have found that the sweet spot is 16 hours of video, which is long enough to teach a topic comprehensively, but not overwhelming. Courses over 16 hours of video gets the maximum score.
The average video length is 8 hours 47 minutes of 111 Deep Learning courses on Udemy.
Detail Score: 10.0 / 10

The top online course contains a detailed description of the course, what you will learn and also a detailed description about the instructor.

Extra Content Score: 5.5 / 10

Tests, exercises, articles and other resources help students to better understand and deepen their understanding of the topic.

This course contains:

0 article.
0 resource.
0 exercise.
0 tests or quizzes.

Table of contents

Description

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)

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)

Requirements

• Understand what deep learning is for and how it is used
• Decent Python coding skills, especially tools for data science (Numpy, Matplotlib)
• Preferable to have experience with RNNs, LSTMs, and GRUs
• Preferable to have experience with Keras
• Preferable to understand word embeddings

This course is for

• 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

How much does the Deep Learning: Advanced NLP and RNNs course cost? Is it worth it?

The course costs $21.99. And currently there is a 82% discount on the original price of the course, which was $124.99. So you save $103 if you enroll the course now.
The average price is $15.1 of 111 Deep Learning courses. So this course is 19% more expensive than the average Deep Learning course on Udemy.

Does the Deep Learning: Advanced NLP and RNNs course have a money back guarantee or refund policy?

YES, Deep Learning: Advanced NLP and RNNs has a 30-day money back guarantee. The 30-day refund policy is designed to allow students to study without risk.

Are there any SCHOLARSHIPS for this course?

Currently we could not find a scholarship for the Deep Learning: Advanced NLP and RNNs course, but there is a $103 discount from the original price ($124.99). So the current price is just $21.99.

Who is the instructor? Is Lazy Programmer Inc. a SCAM or a TRUSTED instructor?

Lazy Programmer Inc. has created 29 courses that got 116,539 reviews which are generally positive. Lazy Programmer Inc. has taught 457,898 students and received a 4.6 average review out of 116,539 reviews. Depending on the information available, Lazy Programmer Inc. is a TRUSTED instructor.
Artificial intelligence and machine learning engineer
Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.

I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition.

Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.

I’ve created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.

My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.

I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. 

Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I’ve used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I’ve used MySQL, Postgres, Redis, MongoDB, and more.

9.3

CourseMarks Score®

9.9

Freshness

9.2

Feedback

8.1

Content

Platform: Udemy
Video: 8h 14m
Language: English
Next start: On Demand

Students are also interested in