Deep Learning:- Recurrent Neural Networks in Python



What can you learn

  • Apply RNN to time series forecasts (handle the issue of "stock forecasts" everywhere)
  • Apply RNN to Natural Language Processing (NLP) and Text Classification (Spam Detection)
  • Apply RNN to image rating
  • Understand simple returning units (German units), GRUs and LSTMs (long-term memory units).
  • Tensorflow 2 - Write different recurring networks.
  • Understand how to reduce the problem of gradual disappearance.

Needs

Basic Mathematics (Etymology, Mathematics, Probability) is useful
Python, Nimbe, Mutual Platform Lab

He described

 NOW IN TENSOR-FLOW 2 & PYTHON 3

Learn about one of the most powerful deep learning architectures ever!

Recurrent Neural Networks (RNNs) are used to obtain the latest results in continuity modeling.

This includes time series analysis, prediction, and natural language processing (NLP).

Learn why old-school machine learning algorithms, such as hidden Markov models, are missing RNNs.

This course will teach you:


Basic information on machine learning and neurons (an overview just for you!)

A network eager to rank and undo (just a review of your practice!)

How to model sequential data

How to model time series data.

How to Model NLP Text Data (With Advanced Text Processing)

How to create an RNN using Tensorflow 2.

How to use GRU and LSTM in Tensorflow 2.

How to predict time series using Tensorflow 2.

How to Predict Stock Prices and Returns Using LSTM in Tensorflow 2 (Warning: Not what you think!)

How to use Tensor Flow 2 Embedding in NLP.

How to create an RNN text label for NLP (examples: spam detection, emotion analysis, segmented speech tagging, component naming).

All materials required for this course can be downloaded and installed for free. We'll be working hard on Numpy, Matplotlib, and Tensorflow. I'm always there to answer your questions and help you on your data science journey.

This course focuses on 'how to create and understand', not just 'how to use'. Anyone can use the API in 15 minutes after reading some documents. It's not about remembering the facts - it's about seeing for yourself through experience. This will teach you how to visualize what is happening internally in the model. If you just want to get a better look at machine learning modules, then this course is for you.

See you in class!



"If you can't do that, you don't understand."

Or, as the great physicist Richard Vanman said: “What I cannot do, I cannot understand.”

My courses are the only course where you will learn how to implement a machine learning algorithm from scratch.

Other courses will teach you how to add your data to the library, but do you really need help with three lines of code?

After you do the same with 10 datasets, you'll realize you didn't learn 10 things. You learn one thing and only repeat the same 3 lines of code 10 times...



Suggested terms:


The sum of the multiplication and multiplication

Basic possibilities (conditional and joint delivery)

Python notation: if / else, loops, lists, dictations, combos

Numpy encoder: matrix and vector operation, CSV file upload.



What rank should I get in your course?:


Check out the 'Machine Learning and Advanced Roadmap for Artificial Intelligence' lecture (including the free course sample in the public survey for any of my courses).

This course is open to all:

Students, professors, anyone with in-depth education, time series forecasting, continuity statistics, or anyone interested in NLP.
Software engineers and data scientists who want to balance their careers.

Here is the download links

https://www.udemy.com/course/deep-learning-recurrent-neural-networks-in-python/
https://drive.google.com/file/d/15PbsgtWgnze6PjApFruK_2mTHYCpDs0K/view?usp=sharing

* If your in trouble watch the video thanks! *


Torrent software for windows -> Torrent Downloader

you can join over whats app group ->  FREE COURSES 2022

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