Deep Learning: Advanced NLP and RNNs (Udemy.com)

Natural Language Processing with Sequence-to-sequence (seq2seq), Attention, CNNs, RNNs, and Memory Networks!

( 130 Reviews )

Created by: Lazy Programmer Inc.

Produced in 2021

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)

Quality Score

Content Quality

Video Quality

Qualified Instructor

Course Pace

Course Depth & Coverage

Overall Score : 88 / 100

Course Description

Its hard to believe it's been been over a year since I released my first course on Deep Learningwith 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), andword embeddingalgorithms such as word2vec and GloVe.
This course takes you to a higher systems level of thinking.
Since you know how these things work, its 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 translationquestion answeringWe'll take a brief look chatbotsand as youll learn in this course, this problem is actually no different from machine translation and question answering.
To solve these problems, were going to look at some advanced Deep NLP techniques, such as:
bidirectional RNNsseq2seq (sequence-to-sequence)attentionmemory networksAll 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 Matpotlibto 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 scratchOther 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 skillsUnderstand RNNs, CNNs, and word embeddingsKnow how to build, train, and evaluate a neural network in KerasWHATORDERSHOULDITAKEYOURCOURSESIN?:
Check out the lecture "Machine Learning and AIPrerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)Who this course is for:
Students in machine learning, deep learning, artificial intelligence, and data scienceProfessionals in machine learning, deep learning, artificial intelligence, and data scienceAnyone interested in state-of-the-art natural language processing

Time: 8.5h

Instructor Details

Lazy Programmer Inc.

4.4 Rating
130 Reviews

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 includesonline 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 deeplearning 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 wevalidated 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, Postg

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Reviews

4.4

130 total reviews

5 star 4 star 3 star 2 star 1 star

Write a Review

By Magnolia Gutierrez Sanchez on 11/14/2020

great course

By Vincent Courtois on 11/7/2020

So far just what I expected. Good presentation and clear explanations.The multiple review sections included are helpful.

By Yangqiu Hu on 11/7/2020

Wish there was a PyTorch version.

By Yvonne Carmel A Suniga on 10/20/2020

This was a nice learning experience. Thanks to EY for this free course! :)

By Jeyahar S J on 10/14/2020

Just started... and it is a good bootup.

By Ebenezer Tham Mensah on 10/13/2020

I like this course and all the courses that I have taken from this particular instructor. His explanations are thorough and he knows each level and the appropriate approach to use. Hoping to enroll on his other new courses. Thumbs up for this course!!

By Anand Kumar on 9/21/2020

so far so good

By Sandip Dhar on 9/21/2020

It was a quite advanced level course. Though I met the prerequisites, had a hard time to understand few things in my first attempt. Though I could understand them after listening to the videos repeatedly and also writing codes on my own. All the theories were covered and explained by the instructor very well but the coding demonstration part could have been better (detailed) for Attention and Memory Network. It took time for me to complete the course but I have learned a lot and also gained confidence. Overall, it was a very good course though quite complex and difficult.

By Anonymized User on 9/19/2020