The Complete Recurrent Neural Network With Python Course

[LeeAndro]

Published 6/2022MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 2.77 GB | Duration: 6h 54m

latent Dirichlet allocation, out-of-core learning, LSTM, and so much more

What you'll learn
Text analysis
Image analysis
Embedding layers
Word embedding
Long short-term memory models
Sequence-to-vector models
Vector-to-sequence models
Bi-directional LSTM
Sequence-to-sequence models
Transfog words into feature vectors
frequency-inverse document frequency
Cleaning text data
Processing documents into tokens
Topic modeling with latent Dirichlet allocation
Decomposing text documents with LDA
Autoencoder
Numpy
Pandas
Tensorflow
Sennt Analysis
Matplotlib
out-of-core learning
Requirements
Basic Python and machine learning knowledge is required
Description
Interested in the field of Machine Learning, Deep Learning, and Artificial Intelligence​

Then this course is for you!This course has been designed by a software eeer. I hope with the experience and knowledge I did gain throughout the years, I can share my knowledge and help you learn complex theories, algorithms, and coding libraries in a simple way.I will walk you step-by-step into Deep Learning. With every tutorial, you will develop new skills and improve your understanding of this challeg yet lucrative sub-field of Data Science.This course is fun and exciting, but at the same , we dive deep into Recurrent Neural Network. Throughout the brand new version of the course, we cover tons of tools and technologies includingeep Learning.Google ColabKeras.Matplotlib.Splitting Data into Training Set and Test Set. Training Neural Network.Model building.Analyzing Results.Model compilation.Make a Prediction.Testing Accuracy.Confusion Matrix.ROC Curve.Text analysis.Image analysis.Embedding layers.Word embedding.Long short-term memory (LSTM) models.Sequence-to-vector models.Vector-to-sequence models.Bi-directional LSTM.Sequence-to-sequence models.Transfog words into feature vectors.frequency-inverse document frequency.Cleaning text data.Processing documents into tokens.Topic modelling with latent Dirichlet allocationDecomposing text documents with LDA.Autoencoder.Numpy.Pandas.Tensorflow.Sennt Analysis.Matplotlib.out-of-core learning.Bi-directional LSTM.Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models. There are several projects for you to practice and build up your knowledge. These projects are listed below:Bitcoin PredictionStock Price PredictionMovie Review senntIMDB Project.MNIST Project.

Overview

Section 1: Introduction

Lecture 1 Course structure

Lecture 2 How to make the most out of this course

Lecture 3 Explanation of tools in this course

Lecture 4 What is the prerequisite of this course

Section 2: Recurrent Neural Network (fundamental)

Lecture 5 Introduction to Recurrent Neural Network (RNN)

Lecture 6 Introduction to Long short term Memory (LSTM)

Lecture 7 Bitcoin prediction Part 1

Lecture 8 Bitcoin prediction Part 2

Lecture 9 Bitcoin prediction Final Part

Section 3: Stock price Prediction

Lecture 10 Apple Stock Price prediction with 50 neurons Part 1

Lecture 11 Apple Stock Price prediction with 50 neurons Part 2

Lecture 12 Apple Stock Price prediction with 100 neurons

Lecture 13 Microsoft's Stock Price Prediction with Added Regularization

Lecture 14 Microsoft's Stock Price Prediction with 100 neurons

Section 4: Sennt Analysis

Lecture 15 Introduction to Natural Language Processing (NLP) and sennt analysis

Lecture 16 Movie sennt analysis project Part 1

Lecture 17 Movie sennt analysis project Part 2

Lecture 18 Movie sennt analysis project Part 3

Lecture 19 Movie sennt analysis project Part 4

Lecture 20 Movie sennt analysis project Part 5

Lecture 21 Movie sennt analysis project Part 6

Lecture 22 Movie sennt analysis project Part 7

Lecture 23 Movie sennt analysis project Part 8

Lecture 24 Movie sennt analysis project Part 9

Lecture 25 Movie sennt analysis project Part 10

Lecture 26 Movie sennt analysis project Part 11

Lecture 27 Movie sennt analysis project Final Part

Section 5: IMDB Project

Lecture 28 Introduction to simple RNN and embedding layer

Lecture 29 IMDB Project Part 1

Lecture 30 IMDB Project Part 2

Lecture 31 IMDB Project Part 3

Lecture 32 IMDB Project Part 4

Lecture 33 IMDB Project Final Part

Lecture 34 MNIST Project Part 1

Lecture 35 MNIST Project Part 2

Lecture 36 MNIST Project Part 3

Lecture 37 MNIST Project Part 4

Lecture 38 MNIST Project Final Part

Section 6: Thank you

Lecture 39 Thank you

Anyone interested in Deep Learning, Machine Learning and Artificial Intelligence,Students who have at least high school knowledge in math and who want to start learning Machine Learning, Deep Learning, and Artificial Intelligence,Any data analysts who want to level up in Machine Learning, Deep Learning and Artificial Intelligence.,Anyone passionate about Artificial Intelligence,Data Scientists who want to take their AI Skills to the next level

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