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Deep Learning: Recurrent Neural Networks in Python

GRU, LSTM, Time Series Forecasting, Stock Predictions, Natural Language Processing (NLP) using Artificial Intelligence
4.6
4.6/5
(3,880 reviews)
28,587 students
Created by

9.3

Classbaze Grade®

10.0

Freshness

9.0

Popularity

8.3

Material

GRU
Platform: Udemy
Video: 12h 4m
Language: English
Next start: On Demand

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Classbaze Rating

Classbaze Grade®

9.3 / 10

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

Freshness

10.0 / 10
This course was last updated on 5/2022.

Course content can become outdated quite quickly.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.

Popularity

9.0 / 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.

Material

8.3 / 10
Video Score: 9.4 / 10
The course includes 12h 4m 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 7 hours 31 minutes of 1,582 Python 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 test.

In this page

About the course

*** NOW IN TENSORFLOW 2 and PYTHON 3 ***
Learn about one of the most powerful Deep Learning architectures yet!
The Recurrent Neural Network (RNN) has been used to obtain state-of-the-art results in sequence modeling.
This includes time series analysis, forecasting and natural language processing (NLP).
Learn about why RNNs beat old-school machine learning algorithms like Hidden Markov Models.
This course will teach you:
•The basics of machine learning and neurons (just a review to get you warmed up!)
•Neural networks for classification and regression (just a review to get you warmed up!)
•How to model sequence data
•How to model time series data
•How to model text data for NLP (including preprocessing steps for text)
•How to build an RNN using Tensorflow 2
•How to use a GRU and LSTM in Tensorflow 2
•How to do time series forecasting with Tensorflow 2
•How to predict stock prices and stock returns with LSTMs in Tensorflow 2 (hint: it’s not what you think!)
•How to use Embeddings in Tensorflow 2 for NLP
•How to build a Text Classification RNN for NLP (examples: spam detection, sentiment analysis, parts-of-speech tagging, named entity recognition)
All of the materials required for this course can be downloaded and installed for FREE. We will do most of our work in Numpy, Matplotlib, and Tensorflow. 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:
•matrix addition, multiplication
•basic probability (conditional and joint distributions)
•Python coding: if/else, loops, lists, dicts, sets
•Numpy coding: matrix and vector operations, loading a CSV file

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)

What can you learn from this course?

✓ Apply RNNs to Time Series Forecasting (tackle the ubiquitous “Stock Prediction” problem)
✓ Apply RNNs to Natural Language Processing (NLP) and Text Classification (Spam Detection)
✓ Apply RNNs to Image Classification
✓ Understand the simple recurrent unit (Elman unit), GRU, and LSTM (long short-term memory unit)
✓ Write various recurrent networks in Tensorflow 2
✓ Understand how to mitigate the vanishing gradient problem

What you need to start the course?

• Basic math (taking derivatives, matrix arithmetic, probability) is helpful
• Python, Numpy, Matplotlib

Who is this course is made for?

• Students, professionals, and anyone else interested in Deep Learning, Time Series Forecasting, Sequence Data, or NLP
• Software Engineers and Data Scientists who want to level up their career

Are there coupons or discounts for Deep Learning: Recurrent Neural Networks in Python ? What is the current price?

The course costs $17.99. And currently there is a 82% discount on the original price of the course, which was $99.99. So you save $82 if you enroll the course now.
The average price is $20.1 of 1,582 Python courses. So this course is 10% cheaper than the average Python course on Udemy.

Will I be refunded if I'm not satisfied with the Deep Learning: Recurrent Neural Networks in Python course?

YES, Deep Learning: Recurrent Neural Networks in Python has a 30-day money back guarantee. The 30-day refund policy is designed to allow students to study without risk.

Are there any financial aid for this course?

Currently we could not find a scholarship for the Deep Learning: Recurrent Neural Networks in Python course, but there is a $82 discount from the original price ($99.99). So the current price is just $17.99.

Who will teach this course? Can I trust Lazy Programmer Inc.?

Lazy Programmer Inc. has created 31 courses that got 131,953 reviews which are generally positive. Lazy Programmer Inc. has taught 494,363 students and received a 4.6 average review out of 131,953 reviews. Depending on the information available, we think that Lazy Programmer Inc. is an instructor that you can trust.
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 first masters degree over a decade ago in computer engineering with a specialization in machine learning and pattern recognition. I received my second masters degree in statistics with applications to financial engineering.
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.
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9.3

Classbaze Grade®

10.0

Freshness

9.0

Popularity

8.3

Material

Platform: Udemy
Video: 12h 4m
Language: English
Next start: On Demand

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