CNN for Computer Vision with Keras and TensorFlow in R

您正在寻找一门完整的卷积神经网络（CNN）课程，该课程教您在R中创建图像识别模型所需的一切，对吗？

您已经找到了正确的卷积神经网络课程！

完成本课程后，您将能够：

识别可以使用CNN模型解决的图像识别问题。

使用Keras和Tensorflow库在R中创建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

对高级图像识别模型（例如LeNet，GoogleNet，VGG16等）有清晰的了解。

你为什么要选择这门课程？

本课程涵盖了使用卷积神经网络创建图像识别模型应采取的所有步骤。

大多数课程仅着眼于如何进行分析的教学，但我们认为，对概念有深刻的理论理解可以使我们创建一个好的模型。在进行分析之后，人们应该能够判断模型的质量并解释结果，从而真正能够为企业提供帮助。

本课程涵盖什么内容？

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。

以下是本课程有关ANN的课程内容：

第1部分（第2部分）-使用R速成课程设置R和R Studio

这部分让您开始使用R。

本节将帮助您在系统上设置R and R studio，并将教您如何在R中执行一些基本操作。

第2部分（第3-6部分）-ANN理论概念

本部分将使您对神经网络中涉及的概念有扎实的理解。

在本节中，您将学习有关单个单元或感知器以及如何堆叠感知器以创建网络体系结构的信息。设置好体系结构后，我们将了解梯度下降算法以查找函数的最小值，并了解如何使用该函数来优化我们的网络模型。

第3部分（第7-11节）-在R中创建ANN模型

在这一部分中，您将学习如何在R中创建ANN模型。

我们将通过使用顺序API创建ANN模型来解决分类问题来开始本节。我们学习如何定义网络体系结构，配置模型和训练模型。然后，我们评估经过训练的模型的性能，并用它来预测新数据。最后，我们学习如何保存和还原模型。

我们还了解这部分库（例如Keras和TensorFlow）的重要性。

第4部分（第12部分）-CNN理论概念

在这一部分中，您将学习卷积层和池化层，它们是CNN模型的基础。

在本节中，我们将从卷积层，步幅，过滤器和特征图的基本理论入手。我们还将说明灰度图像与彩色图像有何不同。最后，我们讨论了在我们的模型中带来计算效率的池化层。

第5部分（第13-14节）-在R中创建CNN模型
在这一部分中，您将学习如何在R中创建CNN模型。

我们将面临识别时尚对象的相同问题，并对其应用CNN模型。我们将CNN模型与ANN模型的性能进行比较，并注意到使用CNN时，准确性提高了9-10％。但是，这还没有结束。我们可以使用在下一部分中探讨的某些技术来进一步提高准确性。

第6部分（第15-18节）-R中的端到端图像识别项目
在本节中，我们将在彩色图像上构建完整的图像识别项目。

我们参加了Kaggle图像识别比赛，并建立了CNN模型来解决该问题。使用简单的模型，我们可以在测试装置上达到近70％的精度。然后，我们学习诸如数据增强和传输学习之类的概念，这些概念可以帮助我们将准确度水平从70％提高到接近97％（与该竞赛的获胜者一样好）。

在本课程结束时，您对在R中创建卷积神经网络模型的信心将会大大提高。您将对如何使用CNN创建预测模型和解决图像识别问题有透彻的了解。

MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 52 lectures (6 hour, 6 mins) | Size: 2.66 GB
Deep Learning models based on Convolutional Neural Networks (CNN) for Image recognition using Keras and Tensorflow in R

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 R
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 sameStudents will need to install R and R Studio 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 R, 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 R 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 R 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)- Setting up R and R Studio with R crash course

This part gets you started with R.

This section will help you set up the R and R studio on your system and it’ll teach you how to perform some basic operations in R.

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 R

In this part you will learn how to create ANN models in R.

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 R
In this part you will learn how to create CNN models in R.

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 R
In 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 R 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

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

Why use R for Deep Learning?

Understanding R is one of the valuable skills needed for a career in Machine Learning. Below are some reasons why you should learn Deep learning in R

1. It’s a popular language for Machine Learning at top tech firms. Almost all of them hire data scientists who use R. Facebook, for example, uses R to do behavioral analysis with user post data. Google uses R to assess ad effectiveness and make economic forecasts. And by the way, it’s not just tech firms: R is in use at analysis and consulting firms, banks and other financial institutions, academic institutions and research labs, and pretty much everywhere else data needs analyzing and visualizing.

2. Learning the data science basics is arguably easier in R. R has a big advantage: it was designed specifically with data manipulation and analysis in mind.

3. Amazing packages that make your life easier. Because R was designed with statistical analysis in mind, it has a fantastic ecosystem of packages and other resources that are great for data science.

4. Robust, growing community of data scientists and statisticians. As the field of data science has exploded, R has exploded with it, becoming one of the fastest-growing languages in the world (as measured by StackOverflow). That means it’s easy to find answers to questions and community guidance as you work your way through projects in R.

5. Put another tool in your toolkit. No one language is going to be the right tool for every job. Adding R to your repertoire will make some projects easier – and of course, it’ll also make you a more flexible and marketable employee when you’re looking for jobs in data science.

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