10最好的神经网络教程推荐

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特写 iPhone，显示 Udemy 应用程序和带笔记本的笔记本电脑有数以千计的在线课程和课程可以帮助您提高 神经网络 技能并获得 神经网络 证书。

在这篇博客文章中，我们的专家汇总了 10 个精选列表 最好的 神经网络 课程, 现在在线提供的教程、培训计划、课程和认证。

我们只包括那些符合我们高质量标准的课程。我们花了很多时间和精力来为您收集这些。这些课程适合所有级别的初学者、中级学习者和专家。

以下是这些课程以及它们为您提供的内容！

10最好的神经网络教程推荐

课程名 在校生（人数） 评论（计数）
1. Deep Learning A-Z™: Hands-On Artificial Neural Networks 我们的最佳选择 339115+ 40911+
2. Neural Networks in Python: Deep Learning for Beginners 119088+ 1159+
3. Artificial Neural Networks for Business Managers in R Studio 97597+ 482+
4. Deep Learning: Recurrent Neural Networks in Python 29522+ 3956+
5. Artificial Intelligence II – Hands-On Neural Networks (Java) 5015+ 474+
6. Introduction to Artificial Neural Network and Deep Learning 2960+ 585+
7. Neural Networks in Python from Scratch: Complete guide 2530+ 288+
8. Practical Neural Networks & Deep Learning In R 1703+ 208+
9. neural networks for sentiment and stock price prediction 1336+ 235+
10. Artificial Neural Network and Machine Learning using MATLAB 792+ 232+

1. Deep Learning A-Z™: Hands-On Artificial Neural Networks 经过 “Kirill Eremenko, Hadelin de Ponteves, Ligency I Team, Ligency Team” Udemy课程 我们的最佳选择

Learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts. Templates included.

截至目前，超过 339115+ 人们已经注册了这门课程，而且已经结束了 40911+ 评论.

课程内容
Welcome to the course
——————— Part 1 – Artificial Neural Networks ———————
ANN Intuition
Building an ANN
——————– Part 2 – Convolutional Neural Networks ——————–
CNN Intuition
Building a CNN
———————- Part 3 – Recurrent Neural Networks ———————-
RNN Intuition
Building a RNN
Evaluating and Improving the RNN
———————— Part 4 – Self Organizing Maps ————————
SOMs Intuition
Building a SOM
Mega Case Study
————————- Part 5 – Boltzmann Machines ————————-
Boltzmann Machine Intuition
Building a Boltzmann Machine
—————————- Part 6 – AutoEncoders —————————-
AutoEncoders Intuition
Building an AutoEncoder
——————- Annex – Get the Machine Learning Basics ——————-
Regression & Classification Intuition
Data Preprocessing Template
Logistic Regression Implementation
Bonus Lectures

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2. Neural Networks in Python: Deep Learning for Beginners 经过 Start-Tech Academy Udemy课程

Learn Artificial Neural Networks (ANN) in Python. Build predictive deep learning models using Keras & Tensorflow| Python

截至目前，超过 119088+ 人们已经注册了这门课程，而且已经结束了 1159+ 评论.

课程内容
Introduction
Setting up Python and Jupyter Notebook
Single Cells – Perceptron and Sigmoid Neuron
Neural Networks – Stacking cells to create network
Important concepts: Common Interview questions
Standard Model Parameters
Practice Test
Tensorflow and Keras
Python – Dataset for classification problem
Python – Building and training the Model
Python – Solving a Regression problem using ANN
Complex ANN Architectures using Functional API
Saving and Restoring Models
Hyperparameter Tuning
Add-on 1: Data Preprocessing
Add-on 2: Classic ML models – Linear Regression
Practice Assignment
Congratulations & about your certificate

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3. Artificial Neural Networks for Business Managers in R Studio 经过 Start-Tech Academy Udemy课程

You do not need coding or advanced mathematics background for this course. Understand how predictive ANN models work

截至目前，超过 97597+ 人们已经注册了这门课程，而且已经结束了 482+ 评论.

课程内容
Introduction
Setting Up R Studio and R crash course
Single Cells – Perceptron and Sigmoid Neuron
Neural Networks – Stacking cells to create network
Important concepts: Common Interview questions
Standard Model Parameters
Practice Test
Tensorflow and Keras
R – Dataset for classification problem
R – Building and training the Model
The NeuralNets Package
R – Complex ANN Architectures using Functional API
Saving and Restoring Models
Hyperparameter Tuning
Add-on 1: Data Preprocessing
Linear Regression Model
Practice Assignment
Congratulations & about your certificate

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4. Deep Learning: Recurrent Neural Networks in Python 经过 Lazy Programmer Inc. Udemy课程

“GRU, LSTM, Time Series Forecasting, Stock Predictions, Natural Language Processing (NLP) using Artificial Intelligence”

截至目前，超过 29522+ 人们已经注册了这门课程，而且已经结束了 3956+ 评论.

课程内容
“Welcome
Google Colab
Machine Learning and Neurons
Feedforward Artificial Neural Networks
Recurrent Neural Networks, Time Series, and Sequence Data
Natural Language Processing (NLP)
In-Depth: Loss Functions
In-Depth: Gradient Descent
Extras
Setting Up Your Environment (FAQ by Student Request)
Extra Help With Python Coding for Beginners (FAQ by Student Request)
Effective Learning Strategies for Machine Learning (FAQ by Student Request)
Appendix / FAQ Finale”

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5. Artificial Intelligence II – Hands-On Neural Networks (Java) 经过 Holczer Balazs Udemy课程

“Hopfield networks, neural networks, gradient descent and backpropagation algorithms explained step by step”

截至目前，超过 5015+ 人们已经注册了这门课程，而且已经结束了 474+ 评论.

课程内容
Introduction
Artificial Intelligence Basics
Hopfield Neural Network Theory
Hopfield Neural Network Implementation
Neural Networks With Backpropagation Theory
Single Perceptron Model
Backpropagation Implementation
Logical Operators
Clustering
Classification – Iris Dataset
Optical Character Recognition (OCR)
Course Materials (DOWNLOADS)

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6. Introduction to Artificial Neural Network and Deep Learning 经过 Seyedali Mirjalili Udemy课程

The Best Machine Learning Techniques for Data Science in Java and Neuroph with Application in Image Recognition

截至目前，超过 2960+ 人们已经注册了这门课程，而且已经结束了 585+ 评论.

课程内容
“Preliminaries and Essential Definitions in Artificial Neural Networks
An Artificial Neuron (Perceptron)
Learning: How to train a Perceptron
A Perceptron Network, Deep Neural Networks, and deep learning
BP: Backpropagation Algorithm
Regression using Neural Networks
Neuroph
Free e-book”

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7. Neural Networks in Python from Scratch: Complete guide 经过 “Jones Granatyr, Ligency I Team, Ligency Team, IA Expert Academy” Udemy课程

Learn the fundamentals of Deep Learning of neural networks in Python both in theory and practice!

截至目前，超过 2530+ 人们已经注册了这门课程，而且已经结束了 288+ 评论.

课程内容
Introduction
Single layer perceptron
Multilayer perceptron
Libraries for neural networks

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8. Practical Neural Networks & Deep Learning In R 经过 Minerva Singh Udemy课程

Artificial Intelligence & Machine Learning for Practical Data Science in R

截至目前，超过 1703+ 人们已经注册了这门课程，而且已经结束了 208+ 评论.

课程内容
INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools
Introduction to Artificial Neural Networks (ANN)
Start With Deep Neural Network (DNN)
ANN & DNN With MXNet Package in R
Convolution Neural Networks (CNN)

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9. neural networks for sentiment and stock price prediction 经过 Dan We Udemy课程

How to predict stock prices with neural networks and sentiment with neural networks. Machine learning hands on data scie

截至目前，超过 1336+ 人们已经注册了这门课程，而且已经结束了 235+ 评论.

课程内容
LSTM neural networks for stock price prediction
Sentiment prediction with LSTM neural networks
NEW BONUS – Predicting the next X days in the future with LSTM Models (multiple)

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10. Artificial Neural Network and Machine Learning using MATLAB 经过 Nastaran Reza Nazar Zadeh Udemy课程

Learn to Create Neural Network with Matlab Toolbox and Easy to Follow Codes; with Comprehensive Theoretical Concepts

截至目前，超过 792+ 人们已经注册了这门课程，而且已经结束了 232+ 评论.

课程内容
Introduction
Artificial Intelligence and Machine Learning
Fundamentals of Artificial Neural Network
MATLAB: Neural Net Fitting Tool
MATLAB: Scripts
MATLAB: Modified Advance Script
MATLAB: Engine Data Set (Multiple Targets)

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下面是一些关于学习的常见问题神经网络

学习神经网络需要多长时间？

“学习神经网络需要多长时间”这个问题的答案是。 . .这取决于。每个人都有不同的需求，每个人都在不同的场景下工作，所以一个人的答案可能与另一个人的答案完全不同。

考虑这些问题：你想学习 神经网络 是为了什么？你的出发点在哪里？您是初学者还是有使用 神经网络 的经验？你能练习多少？每天1小时？每周40小时？ 查看本课程关于 神经网络.

神经网络 学起来容易还是难？

不，学习 神经网络 对大多数人来说并不难。检查这个 关于如何学习的课程 神经网络 立刻！

如何快速学习神经网络？

学习 神经网络 最快的方法是先得到这个 神经网络 课程, 然后尽可能练习你学到的任何东西。即使每天只有 15 分钟的练习。一致性是关键.

在哪里学习 神经网络？

如果您想探索和学习 神经网络，那么 Udemy 为您提供了学习 神经网络 的最佳平台。查看此 关于如何学习的课程 神经网络 立刻！