The Complete Recurrent Neural Network With Python Course

对机器学习、深度学习和人工智能领域感兴趣？那么本课程适合您！本课程由软件工程师设计。我希望通过我这些年来获得的经验和知识，我可以分享我的知识，帮助您以简单的方式学习复杂的理论、算法和编码库。我将逐步带您进入深度学习。通过每个教程，您将发展新技能并提高您对数据科学这个具有挑战性但利润丰厚的子领域的理解。本课程既有趣又令人兴奋，但与此同时，我们将深入研究循环神经网络。在整个全新版本的课程中，我们涵盖了大量的工具和技术，包括：Deep Learning.Google ColabKeras.Matplotlib.Splitting Data into Training Set 和 Test Set。训练神经网络。模型构建.分析结果.模型编译.进行预测.测试准确性.混淆矩阵.ROC曲线.文本分析.图像分析.嵌入层.词嵌入.长短期记忆（LSTM）模型.序列到向量模型.Vector-to-sequence 模型.Bi-directional LSTM.Sequence-to-sequence 模型.将单词转换为特征向量.频率-逆文档频率.清理文本数据.将文档处理为令牌.使用潜在 Dirichlet 分配的主题建模分解文本文档LDA.Autoencoder.Numpy.Pandas.Tensorflow.Sentiment Analysis.Matplotlib.out-of-core learning.Bi-directional LSTM。此外，该课程还包含基于现实生活示例的实践练习。因此，您不仅会学习理论，还会获得一些动手实践来构建自己的模型。有几个项目供您练习和积累知识。这些项目在下面列出：比特币预测股票价格预测电影评论情绪IMDB 项目.MNIST 项目。

概述

第 1 节：简介

第一讲 课程结构

第 2 讲 如何充分利用本课程

Lecture 3 本课程的工具说明

第4讲 本课程的先决条件是什么

第 2 节：循环神经网络（基础）

第5讲递归神经网络（RNN）简介

第 6 讲 长短期记忆 (LSTM) 简介

第 7 讲比特币预测第 1 部分

第 8 讲比特币预测第 2 部分

第9讲比特币预测Final Part

第三部分：股价预测

第 10 讲 50 个神经元的苹果股票价格预测第 1 部分

第 11 讲用 50 个神经元预测苹果股票价格第 2 部分

第 12 讲 100 个神经元的苹果股票价格预测

第 13 讲微软的股票价格预测加上正则化

第 14 讲 100 个神经元的微软股票价格预测

第 4 节：情绪分析

第15讲自然语言处理（NLP）和情感分析简介

Lecture 16 电影情感分析项目 Part 1

Lecture 17 电影情感分析项目 Part 2

第18讲电影情感分析项目Part 3

Lecture 19 电影情感分析项目 Part 4

Lecture 20 电影情感分析项目 Part 5

第21讲电影情感分析项目第6部分

Lecture 22 电影情感分析项目 Part 7

第23讲电影情感分析项目第8部分

第24讲电影情感分析项目第9部分

第25讲电影情感分析项目第10部分

第26讲电影情感分析项目第11部分

第27讲电影情感分析项目Final Part

第 5 部分：IMDB 项目

第28讲简单RNN和嵌入层介绍

第 29 讲 IMDB 项目第 1 部分

第 30 讲 IMDB 项目第 2 部分

第 31 讲 IMDB 项目第 3 部分

第 32 讲 IMDB 项目第 4 部分

Lecture 33 IMDB Project Final Part

第 34 讲 MNIST 项目第 1 部分

第 35 讲 MNIST 项目第 2 部分

第 36 讲 MNIST 项目第 3 部分

第 37 讲 MNIST 项目第 4 部分

第 38 讲 MNIST 项目最终部分

第六部分：谢谢

第39讲 谢谢

任何对深度学习、机器学习和人工智能感兴趣的人，至少具有高中数学知识并想开始学习机器学习、深度学习和人工智能的学生，任何想在机器学习方面升级的数据分析师，深度学习和人工智能。,任何对人工智能充满热情的人,想要将人工智能技能提升到新水平的数据科学家

Published 6/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: 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
Transforming 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
Sentiment 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 engineer. 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 challenging yet lucrative sub-field of Data Science.This course is fun and exciting, but at the same time, we dive deep into Recurrent Neural Network. Throughout the brand new version of the course, we cover tons of tools and technologies including:Deep 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.Transforming 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.Sentiment 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 sentimentIMDB 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: Sentiment Analysis

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

Lecture 16 Movie sentiment analysis project Part 1

Lecture 17 Movie sentiment analysis project Part 2

Lecture 18 Movie sentiment analysis project Part 3

Lecture 19 Movie sentiment analysis project Part 4

Lecture 20 Movie sentiment analysis project Part 5

Lecture 21 Movie sentiment analysis project Part 6

Lecture 22 Movie sentiment analysis project Part 7

Lecture 23 Movie sentiment analysis project Part 8

Lecture 24 Movie sentiment analysis project Part 9

Lecture 25 Movie sentiment analysis project Part 10

Lecture 26 Movie sentiment analysis project Part 11

Lecture 27 Movie sentiment 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