Convolutional Neural Networks in Python: CNN Computer Vision
用于计算机视觉和图像识别的Python深度学习卷积神经网络(CNN) – Keras & TensorFlow 2

你会学到:
深入了解卷积神经网络和深度学习
Python构建一个端到端的图像识别项目
了解Keras和Tensorflow库的用法
使用人工神经网络进行预测
使用熊猫数据帧来处理数据和进行统计计算。

时长:7h 40m |视频:. MP4,1280×720 30 fps |音频:AAC,44.1 kHz,2ch |大小解压后:2.83 GB
语言:英语+中英文字幕(云桥网络 机译)


要求:
学生将需要安装Python和Anaconda软件,但是我们有一个单独的讲座来帮助您安装相同的软件

描述:
你正在寻找一个完整的卷积神经网络(CNN)课程,它会教你用Python创建图像识别模型所需的一切,对吗?
你找到了正确的卷积神经网络课程!

完成本课程后,您将能够:
确定可以使用有线电视新闻网模型解决的图像识别问题。
使用Keras和Tensorflow库用Python创建CNN模型,并分析其结果。
自信地练习、讨论和理解深度学习概念
清楚了解高级图像识别模型,如LeNet、GoogleNet、VGG16等。

这门课对你有什么帮助?
一份可验证的结业证书将提交给所有学习卷积神经网络课程的学生。
如果你是一名分析师或ML科学家,或者是一名想要在真实世界图像识别问题中学习和应用深度学习的学生,本课程将为你提供一个坚实的基础,通过教你一些最先进的深度学习概念及其在Python中的实现,而不会变得过于数学化。

为什么要选择这门课?
本课程涵盖了使用卷积神经网络创建图像识别模型的所有步骤。
大多数课程只侧重于教授如何进行分析,但我们相信,对概念有很强的理论理解使我们能够创建一个好的模型。在运行分析之后,人们应该能够判断模型有多好,并将结果解释为实际上能够帮助业务。

我们凭什么有资格教你?
该课程由Abhishek和Pukhraj教授。作为全球分析咨询公司的经理,我们已经使用深度学习技术帮助企业解决了他们的业务问题,并且我们已经利用我们的经验将数据分析的实际方面包括在本课程中
我们也是一些最受欢迎的在线课程的创作者,有超过30万的注册人数和数以千计的5星级评价,

例如:
这很好,我喜欢所有给出的解释都能被一个外行人理解的事实——约书亚
谢谢作者的精彩课程。你是最棒的,这门课值得任何代价。黛西
我们的承诺
教育我们的学生是我们的工作,我们致力于此。如果您对课程内容、练习单或任何相关主题有任何疑问,您可以随时在课程中发布问题或直接向我们发送消息。
下载练习文件,参加练习测试,并完成作业
每堂课都附有课堂笔记,供你跟随。你也可以参加实践测试来检查你对概念的理解。有一个最后的实践作业给你,让你去实践你的学习。

本课程涵盖哪些内容?
本课程教您创建基于神经网络的模型(即深度学习模型)以解决业务问题的所有步骤。

以下是本课程关于人工神经网络的课程内容:
第1部分(第2节)- Python基础
这一部分让您开始使用Python。
这一部分将帮助您在系统上设置python和Jupyter环境,并教您如何用Python执行一些基本操作。我们将了解不同图书馆的重要性,如Numpy、Pandas & Seaborn。

第2部分(第3-6节)-人工神经网络理论概念
这一部分将使你对神经网络中涉及的概念有一个坚实的理解。
在本节中,您将了解单个单元或感知器,以及感知器如何堆叠以创建网络架构。一旦设置了架构,我们就可以理解梯度下降算法来寻找函数的最小值,并了解如何使用它来优化我们的网络模型。

第3部分(第7-11节)-用Python创建人工神经网络模型
在这一部分中,您将学习如何在Python中创建人工神经网络模型。
我们将从使用顺序应用编程接口创建人工神经网络模型来解决分类问题开始这一部分。我们学习如何定义网络架构、配置模型和训练模型。然后我们评估我们训练的模型的性能,并使用它来预测新数据。最后,我们学习如何保存和恢复模型。

在这一部分中,我们还了解了Keras和TensorFlow等库的重要性。

第4部分(第12节)-CNN理论概念
在本部分中,您将了解卷积层和池层,它们是CNN模型的构建块。
在本节中,我们将从卷积层、步长、滤波器和特征映射的基本理论开始。我们还解释了灰度图像与彩色图像的区别。最后,我们讨论了在我们的模型中带来计算效率的池层。

第5部分(第13-14节)-在Python中创建CNN模型在这一部分中,您将学习如何在Python中创建CNN模型。
我们将处理识别时尚对象的相同问题,并应用CNN模型。我们将比较我们的CNN模型和我们的ANN模型的性能,并注意到当我们使用CNN时,准确率提高了9-10%。然而,这并不是结束。我们可以通过使用我们在下一部分中探讨的某些技术来进一步提高精度。

第6部分(第15-18节)-蟒蛇中的端到端图像识别项目本节我们构建了一个完整的彩色图像图像识别项目。
我们采取了一个Kaggle图像识别比赛,并建立了CNN模型来解决这个问题。通过一个简单的模型,我们在测试集上实现了近70%的准确率。然后,我们学习数据增强和转移学习等概念,帮助我们将准确度从70%提高到近97%(与该竞赛的获胜者一样好)。

到本课程结束时,您对用Python创建卷积神经网络模型的信心将大大增强。您将彻底了解如何使用CNN创建预测模型和解决图像识别问题。
继续并单击“注册”按钮,我们将在第1课中看到您!
干杯
创办科技学院
————
下面是一些想开始深入学习之旅的学生的常见问题解答-
为什么要使用Python进行深入学习?
理解Python是深入学习职业所需的宝贵技能之一。
尽管并非总是如此,Python是数据科学的首选编程语言。以下是一段简短的历史:
2016年,它在数据科学竞赛的首要平台Kaggle上超越了R。
2017年,在KDNuggets的年度数据科学家最常用工具调查中,它超越了R。
2018年,66%的数据科学家报告每天都在使用Python,这使它成为分析专业人士的头号工具。
深度学习专家预计,随着Python生态系统的不断发展,这种趋势将继续下去。虽然您学习Python编程的旅程可能才刚刚开始,但很高兴知道就业机会也很丰富(而且还在增长)。
数据挖掘、机器学习和深度学习之间的区别是什么?
简单地说,机器学习和数据挖掘使用与数据挖掘相同的算法和技术,只是预测的种类不同。当数据挖掘发现以前未知的模式和知识时,机器学习再现已知的模式和知识,并进一步自动将这些信息应用到数据、决策和行动中。
另一方面,深度学习使用先进的计算能力和特殊类型的神经网络,并将其应用于大量数据,以学习、理解和识别复杂模式。自动语言翻译和医学诊断是深度学习的例子。

本课程面向谁:
从事数据科学职业的人
在职专业人士开始深入学习之旅
任何想在短时间内掌握初级图像识别的人

We also understand the importance of libraries such as Keras and TensorFlow in this part.
Part 4 (Section 12) – CNN Theoretical Concepts
In this part you will learn about convolutional and pooling layers which are the building blocks of CNN models.
In this section, we will start with the basic theory of convolutional layer, stride, filters and feature maps. We also explain how gray-scale images are different from colored images. Lastly we discuss pooling layer which bring computational efficiency in our model.
Part 5 (Section 13-14) – Creating CNN model in PythonIn this part you will learn how to create CNN models in Python.
We will take the same problem of recognizing fashion objects and apply CNN model to it. We will compare the performance of our CNN model with our ANN m odel and notice that the accuracy increases by 9-10% when we use CNN. However, this is not the end of it. We can further improve accuracy by using certain techniques which we explore in the next part.
Part 6 (Section 15-18) – End-to-End Image Recognition project in PythonIn this section we build a complete image recognition project on colored images.
We take a Kaggle image recognition competition and build CNN model to solve it. With a simple model we achieve nearly 70% accuracy on test set. Then we learn concepts like Data Augmentation and Transfer Learning which help us improve accuracy level from 70% to nearly 97% (as good as the winners of that competition).
By the end of this course, your confidence in creating a Convolutional Neural Network model in Python will soar. You’ll have a thorough understanding of how to use CNN to create predictive models and solve image recognition problems.

Go ahead and click the enroll button, and I’ll see you in lesson 1!

Cheers
Start-Tech Academy

————
Below are some popular FAQs of students who want to start their Deep learning journey-

Why use Python for Deep Learning?
Understanding Python is one of the valuable skills needed for a career in Deep Learning.
Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history:
In 2016, it overtook R on Kaggle, the premier platform for data science competitions.
In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools.
In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.
Deep Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python program ming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well.
What is the difference between Data Mining, Machine Learning, and Deep Learning?
Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions.
Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.

Who this course is for:
People pursuing a career in data science
Working Professionals beginning their Deep Learning journey
Anyone curious to master image recognition from Beginner level in short span of time

Duration: 7h 40m | Video: .MP4, 1280×720 30 fps | Audio: AAC, 44.1 kHz, 2ch | Size: 2.7 GB
Genre: eLearning | Language: English

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

What you’ll learn:
Get a solid understanding of Convolutional Neural Networks (CNN) and Deep Learning
Build an end-to-end Image recognition project in Python
Learn usage of Keras and Tensorflow libraries
Use Artificial Neural Networks (ANN) to make predictions
Use Pandas DataFrames to manipulate data and make statistical computations.

Requirements:
Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same

Description:
You’re looking for a complete Convolutional Neural Network (CNN) course that teaches you everything you need to create a Image Recognition model in Python, right?
You’ve found the right Convolutional Neural Networks course!
After completing this course you will be able to:
Identify the Image Recognition problems which can be solved using CNN Models.
Create CNN models in Python using Keras and Tensorflow libraries and analyze their results.
Confidently practice, discuss and understand Deep Learning concepts
Have a clear understanding of Advanced Image Recognition models such as LeNet, GoogleNet, VGG16 etc.
How this course will help you?
A Verifiable Certificate of Completion is presented to all students who undertake this Convolutional Neural networks course.
If you are an Analyst or an ML scientist, or a student who wants to learn and apply Deep learning in Real world image recognition problems, this course will give you a solid base for that by teaching you some of the most advanced concepts of Deep Learning and their implementation in Python without getting too Mathematical.
Why should you choose this course?
This course covers all the steps that one should take to create an image recognition model using Convolutional Neural Networks.
Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business.
What makes us qualified to teach you?
The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Deep learning techniques and we have used our experience to include the practical aspects of data analysis in this course
We are also the creators of some of the most popular online courses – with over 300,000 enrollments and thousands of 5-star reviews like these ones:
This is very good, i love the fact the all explanation given can be understood by a layman – Joshua
Thank you Author for this wonderful course. You are the best and this course is worth any price. – Daisy
Our Promise
Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.
Download Practice files, take Practice test, and complete Assignments
With each lecture, there are class notes attached for you to follow along. You can also take practice test to check your understanding of concepts. There is a final practical assignment for you to practically implement your learning.
What is covered in this course?
This course teaches you all the steps of creating a Neural network based model i.e. a Deep Learning model, to solve business problems.
Below are the course contents of this course on ANN:
Part 1 (Section 2)- Python basics
This part gets you started with Python.
This part will help you set up the python and Jupyter environment on your system and it’ll teach you how to perform some basic operations in Python. We will understand the importance of different libraries such as Numpy, Pandas & Seaborn.
Part 2 (Section 3-6) – ANN Theoretical Concepts
This part will give you a solid understanding of concepts involved in Neural Networks.
In this section you will learn about the single cells or Perceptrons and how Perceptrons are stacked to create a network architecture. Once architecture is set, we understand the Gradient descent algorithm to find the minima of a function and learn how this is used to optimize our network model.
Part 3 (Section 7-11) – Creating ANN model in Python
In this part you will learn how to create ANN models in Python.
We will start this section by creating an ANN model using Sequential API to solve a classification problem. We learn how to define network architecture, configure the model and train the model. Then we evaluate the performance of our trained model and use it to predict on new data. Lastly we learn how to save and restore models.
We also understand the importance of libraries such as Keras and TensorFlow in this part.
Part 4 (Section 12) – CNN Theoretical Concepts
In this part you will learn about convolutional and pooling layers which are the building blocks of CNN models.
In this section, we will start with the basic theory of convolutional layer, stride, filters and feature maps. We also explain how gray-scale images are different from colored images. Lastly we discuss pooling layer which bring computational efficiency in our model.
Part 5 (Section 13-14) – Creating CNN model in PythonIn this part you will learn how to create CNN models in Python.
We will take the same problem of recognizing fashion objects and apply CNN model to it. We will compare the performance of our CNN model with our ANN model and notice that the accuracy increases by 9-10% when we use CNN. However, this is not the end of it. We can further improve accuracy by using certain techniques which we explore in the next part.
Part 6 (Section 15-18) – End-to-End Image Recognition project in PythonIn this section we build a complete image recognition project on colored images.
We take a Kaggle image recognition competition and build CNN model to solve it. With a simple model we achieve nearly 70% accuracy on test set. Then we learn concepts like Data Augmentation and Transfer Learning which help us improve accuracy level from 70% to nearly 97% (as good as the winners of that competition).
By the end of this course, your confidence in creating a Convolutional Neural Network model in Python will soar. You’ll have a thorough understanding of how to use CNN to create predictive models and solve image recognition problems.

Go ahead and click the enroll button, and I’ll see you in lesson 1!

Cheers
Start-Tech Academy

————
Below are some popular FAQs of students who want to start their Deep learning journey-

Why use Python for Deep Learning?
Understanding Python is one of the valuable skills needed for a career in Deep Learning.
Though it hasn’t always been, Python is the programming language of choice for data science. Here’s a brief history:
In 2016, it overtook R on Kaggle, the premier platform for data science competitions.
In 2017, it overtook R on KDNuggets’s annual poll of data scientists’ most used tools.
In 2018, 66% of data scientists reported using Python daily, making it the number one tool for analytics professionals.
Deep Learning experts expect this trend to continue with increasing development in the Python ecosystem. And while your journey to learn Python programming may be just beginning, it’s nice to know that employment opportunities are abundant (and growing) as well.
What is the difference between Data Mining, Machine Learning, and Deep Learning?
Put simply, machine learning and data mining use the same algorithms and techniques as data mining, except the kinds of predictions vary. While data mining discovers previously unknown patterns and knowledge, machine learning reproduces known patterns and knowledge—and further automatically applies that information to data, decision-making, and actions.
Deep learning, on the other hand, uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning.

Who this course is for:
People pursuing a career in data science
Working Professionals beginning their Deep Learning journey
Anyone curious to master image recognition from Beginner level in short span of time

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