10最好的卷积神经网络教程推荐

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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 卷积神经网络 技能并获得 卷积神经网络 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 卷积神经网络 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最好的卷积神经网络教程推荐

1. Convolutional Neural Networks in Python: CNN Computer Vision 经过 Start-Tech Academy Udemy课程 我们的最佳选择

Python for Computer Vision & Image Recognition – Deep Learning Convolutional Neural Network (CNN) – Keras & TensorFlow 2

截至目前,超过 117856+ 人们已经注册了这门课程,而且已经结束了 1123+ 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
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
CNN – Basics
Creating CNN model in Python
Analyzing impact of Pooling layer
Project : Creating CNN model from scratch
Project : Data Augmentation for avoiding overfitting
Transfer Learning : Basics
Transfer Learning in Python
Congratulations & about your certificate

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2. Image Recognition for Beginners using CNN in R Studio 经过 Start-Tech Academy Udemy课程

Deep Learning based Convolutional Neural Networks (CNN) for Image recognition using Keras and Tensorflow in R Studio

截至目前,超过 78423+ 人们已经注册了这门课程,而且已经结束了 256+ 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
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
CNN – Basics
Creating CNN model in R
Analyzing impact of Pooling layer
Project : Creating CNN model from scratch
Project : Data Augmentation for avoiding overfitting
Transfer Learning : Basics
Transfer Learning in R
Congratulations & about your certificate

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3. Deep Learning: Convolutional Neural Networks in Python 经过 Lazy Programmer Inc. Udemy课程

“Tensorflow 2 CNNs for Computer Vision, Natural Language Processing (NLP) +More! For Data Science & Machine Learning”

截至目前,超过 31700+ 人们已经注册了这门课程,而且已经结束了 4622+ 评论.

课程内容
Welcome
Google Colab
Machine Learning and Neurons
Feedforward Artificial Neural Networks
Convolutional Neural Networks
Natural Language Processing (NLP)
Convolution In-Depth
Convolutional Neural Network Description
Practical Tips
In-Depth: Loss Functions
In-Depth: Gradient Descent
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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4. Deep Learning with Keras and Tensorflow in R 经过 Bogdan Anastasiei Udemy课程

“Learn to use convolutional neural networks for image recognition, character recognition and accurate predictions.”

截至目前,超过 5169+ 人们已经注册了这门课程,而且已经结束了 13+ 评论.

课程内容
Getting Started
Basic Notions
Building Classification Models with CNNS
Recognizing Human Faces From Trees
Recognizing Animals
Telling Asterisks From Hashtags
Recognizing Hand-Written Numbers
Practice
Useful Links

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5. Deep Learning : Plunge into Deep Learning 经过 Muni Kumar Gopu V R Udemy课程

Learn to create Deep Learning models starting from basics

截至目前,超过 2333+ 人们已经注册了这门课程,而且已经结束了 27+ 评论.

课程内容
Introduction
Fundamentals
Neural Networks
Training Neural Networks
Convolution Neural Networks
Sequence Models

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6. Convolutional Neural Networks with TensorFlow in Python 经过 365 Careers Udemy课程

Advanced neural networks: Master Computer Vision with Convolutional Neural Networks (CNN) and Deep Learning

截至目前,超过 1452+ 人们已经注册了这门课程,而且已经结束了 161+ 评论.

课程内容
Introduction to the course
Kernels
CNN Introduction
Neural networks techniques (revision)
Setting up the environment
CNN assembling – MNIST
Tensorboard: Visualization tool for TensorFlow
Common techniques for better performance of NN
A practical project: Labelling fashion items
Understanding CNNs
Popular CNN architectures

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7. Convolutional Neural Networks for Medical Images Diagnosis 经过 Hussein Samma Udemy课程

“CNN, Deep Learning, Medical Imaging, Transfer Learning, CNN Visualization, VGG, ResNet, Inception, Python & Keras”

截至目前,超过 452+ 人们已经注册了这门课程,而且已经结束了 119+ 评论.

课程内容
Introduction
Getting and Preparing Data
CNN Architecture
CNN Training
CNN Evaluation
CNN Improvement
CNN Transfer Learning
CNN Visualization
CNN Deployment

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8. Convolutional Neural Networks for Image Classification 经过 Valentyn Sichkar Udemy课程

“Design your own deep CNN for accurate image recognition, train and test in Real Time by camera”

截至目前,超过 408+ 人们已经注册了这门课程,而且已经结束了 45+ 评论.

课程内容
Welcome
Assemble custom dataset for Image Classification
Modify existing dataset of Traffic Signs for Classification
Apply preprocessing techniques for datasets before training
Design deep CNNs architectures for efficient Classification
Train and Test designed CNNs models
Practice Test
Generate synthetic data to augment datasets
How does it work?

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9. Deep Learning: CNNs for Visual Recognition 经过 “Skillbox, LLC” Udemy课程

Learn Convolutional Neural Networks for Visual Recognition and the building blocks and methods associated with them.

截至目前,超过 200+ 人们已经注册了这门课程,而且已经结束了 33+ 评论.

课程内容
Welcome
Convolutional Neural Networks
CNN Applications
Visualizations
Image Enhancement
Course Summary

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10. Convolutional Neural Networks with Pytorch 经过 Leo Bravo Udemy课程

Learn how to implement a Convolutional Neural Network using Pytorch

截至目前,超过 62+ 人们已经注册了这门课程,而且已经结束了 12+ 评论.

课程内容
What is a Convolutional Neural Network?
Convolutional Neural Networks with Pytorch

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下面是一些关于学习的常见问题卷积神经网络

学习卷积神经网络需要多长时间?

“学习卷积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 卷积神经网络 是为了什么?你的出发点在哪里?您是初学者还是有使用 卷积神经网络 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 卷积神经网络.

卷积神经网络 学起来容易还是难?

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

如何快速学习卷积神经网络?

学习 卷积神经网络 最快的方法是先得到这个 卷积神经网络 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 卷积神经网络?

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