Deep Learning Recurrent Neural Networks in Python 2021

[Prohibitive]

MP4 | Video: h264, 1280x720 | Audio: AAC, 44100 Hz
Language: English | Size: 3.66 GB | Duration: 11h 54m

Learn about one of the most powerful Deep Learning architectures yet!

What you'll learn

Apply RNNs to Series Fecasting (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 sht-term memy unit)

Write various recurrent netwks in Tensflow 2

Understand how to mitigate the vanishing gradient problem

Requirements

Basic math (taking derivatives, matrix arithmetic, probability) is helpful

Python, Numpy, Matplotlib

Description

*** NOW IN TENSFLOW 2 and PYTHON 3 ***

The Recurrent Neural Netwk (RNN) has been used to obtain state-of-the-art results in sequence modeling.

This includes series analysis, fecasting and natural Language processing (NLP).

Learn about why RNNs beat old-school machine learning algithms 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 netwks f classification and regression (just a review to get you warmed up!)

How to model sequence data

How to model series data

How to model text data f NLP (including preprocessing steps f text)

How to build an RNN using Tensflow 2

How to use a GRU and LSTM in Tensflow 2

How to do series fecasting with Tensflow 2

How to predict stock prices and stock returns with LSTMs in Tensflow 2 (hint: it's not what you think!)

How to use Embeddings in Tensflow 2 f NLP

How to build a Text Classification RNN f NLP (examples: spam detection, sennt analysis, parts-of-speech tagging, named entity recognition)

https://gfxhome.co/wallpaper/

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 f yourself" via expentation. It will teach you how to visualize what's happening in the model internally. If you want me than just a superficial look at machine learning models, this course is f you.

See you in class!

"If you can't implement it, you don't understand it"

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 algithms 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 s...

Suggested Prerequisites:

matrix addition, multiplication

basic probability (conditional and joint distributions)

Python coding: if/else, loops, lists, dicts, sets

Numpy coding: matrix and vect operations, loading a CSV file

WHAT DER 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)

Who this course is f:

Students, professionals, and anyone else interested in Deep Learning, Series Fecasting, Sequence Data, NLP

Software Eeers and Data Scientists who want to level up their career

I recommends Buy premimum account for High speed+parallel downloads!

Rapidgator

https://rapidgator.net/file/4fe652ed2e3dd6f9f964c3db58a3526e/aG9fX1Xr__Deep_Learn.part1.rar.html
https://rapidgator.net/file/d1260bfd2c2658bc641e8fba1d4d2561/aG9fX1Xr__Deep_Learn.part2.rar.html
https://rapidgator.net/file/1722151c43372eca63ee55f982a01c86/aG9fX1Xr__Deep_Learn.part3.rar.html
https://rapidgator.net/file/fa446b08213fe163fda5c7c71727a5df/aG9fX1Xr__Deep_Learn.part4.rar.html

Nitroflare

https://nitro.download/view/53805D2C0B2387C/aG9fX1Xr__Deep_Learn.part1.rar
https://nitro.download/view/19B31C3E8488A47/aG9fX1Xr__Deep_Learn.part2.rar
https://nitro.download/view/241EAEAC284E5D9/aG9fX1Xr__Deep_Learn.part3.rar
https://nitro.download/view/B6F5F104B729222/aG9fX1Xr__Deep_Learn.part4.rar