Deep Learning: Recurrent Neural Networks in Python

Name: Deep Learning: Recurrent Neural Networks in Python
Brand: Udemy
SKU: 43593
Price: 12.99 USD
Availability: InStock

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Lazy Programmer Inc. 47 Machine Learning Udemy

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Reviews (5)

8.7/10 (Our Score)

Product is rated as #88 in category Machine Learning

*** 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.

Instructor Details

Lazy Programmer Inc.Artificial intelligence and machine learning engineer

Votes: 1

Courses : 22

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.

Specification: Deep Learning: Recurrent Neural Networks in Python

Duration	11.5 hours
Year	2020
Level	All
Certificate	Yes
Quizzes	No

5 reviews for Deep Learning: Recurrent Neural Networks in Python

4.6 out of 5

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★★★★★
Priyanka Aishwarya – January 31, 2020
yes
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★★★★★
Charles Johler – February 5, 2020
excellent!
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★★★★★
George Herbert – March 26, 2020
This course wasn’t necessarily what I had anticipated. It was largely what I needed.
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★★★★★
Ivan E Sanchez Garcia – July 14, 2020
The theoretical part of the course (GRU and LSTM) is very poor. I did a different course, so I understood it. In general is OK.
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★★★★★
Ana Gleice da Silva Santos – July 20, 2020
Parece que vou aprender muito
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