Image Recognition For Beginners Using Cnn In R Studio

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Last updated 8/2022MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 2.90 GB | Duration: 6h 34m

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

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
Requirements
Students will need to install R and RStudio 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 an 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 conceptsHave a clear understanding of Advanced Image Recognition models such as LeNet, GoogleNet, VGG16 etc.How this course will help youA 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 courseThis 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 youThe 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 - JoshuaThank you Author for this wonderful course. You are the best and this course is worth any price. - DaisyOur PromiseTeaching 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. Practice files, take Practice test, and complete AssignmentsWith 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 ANNart 1 (Section 2)- Setting up R and R Studio with R crash courseThis 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 ConceptsThis 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 RIn 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 ConceptsIn 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 RIn 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 RIn 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!CheersStart-Tech AcademyBelow are some popular FAQs of students who want to start their Deep learning journey-Why use R for Deep LearningUnderstanding 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, acad 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 LearningPut 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.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Course Resources

Section 2: Setting Up R Studio and R crash course

Lecture 3 Installing R and R studio

Lecture 4 This is a milestone!

Lecture 5 Basics of R and R studio

Lecture 6 Packages in R

Lecture 7 Inputting data part 1: Inbuilt datasets of R

Lecture 8 Inputting data part 2: Manual data entry

Lecture 9 Inputting data part 3: Importing from CSV or Text files

Lecture 10 Creating Barplots in R

Lecture 11 Creating Histograms in R

Section 3: Single Cells - Perceptron and Sigmoid Neuron

Lecture 12 Perceptron

Lecture 13 Activation Functions

Section 4: Neural Networks - Stacking cells to create network

Lecture 14 Basic Teologies

Lecture 15 Gradient Descent

Lecture 16 Back Propagation

Section 5: Important concepts: Common Interview questions

Lecture 17 Some Important Concepts

Section 6: Standard Model Parameters

Lecture 18 Hyperparameters

Section 7: Tensorflow and Keras

Lecture 19 Keras and Tensorflow

Lecture 20 Installing Keras and Tensorflow

Section 8: R - Dataset for classification problem

Lecture 21 Data Normalization and Test-Train Split

Lecture 22 More about test-train split

Section 9: R - Building and training the Model

Lecture 23 Building, Compiling and Training

Lecture 24 Evaluating and Predicting

Section 10: The NeuralNets Package

Lecture 25 ANN with NeuralNets Package

Section 11: R - Complex ANN Architectures using Functional API

Lecture 26 Building Regression Model with Functional AP

Lecture 27 Complex Architectures using Functional API

Section 12: Saving and Restoring Models

Lecture 28 Saving - Restoring Models and Using Callbacks

Section 13: Hyperparameter Tuning

Lecture 29 Hyperparameter Tuning

Section 14: CNN - Basics

Lecture 30 CNN Introduction

Lecture 31 Stride

Lecture 32 Padding

Lecture 33 Filters and Feature maps

Lecture 34 Channels

Lecture 35 PoolingLayer

Section 15: Creating CNN model in R

Lecture 36 CNN on MNIST Fashion Dataset - Model Architecture

Lecture 37 Data Preprocessing

Lecture 38 Creating Model Architecture

Lecture 39 Compiling and training

Lecture 40 Model Performance

Section 16: Analyzing impact of Pooling layer

Lecture 41 Comparison - Pooling vs Without Pooling in R

Section 17: Project : Creating CNN model from scratch

Lecture 42 Project - Introduction

Lecture 43 Data for the project

Lecture 44 Project in R - Data Preprocessing

Lecture 45 CNN Project in R - Structure and Compile

Lecture 46 Project in R - Training

Lecture 47 Project in R - Model Performance

Section 18: Project : Data Augmentation for avoiding overfitting

Lecture 48 Project in R - Data Augmentation

Lecture 49 Project in R - Validation Performance

Section 19: Transfer Learning : Basics

Lecture 50 ILSVRC

Lecture 51 LeNET

Lecture 52 VGG16NET

Lecture 53 GoogLeNet

Lecture 54 Transfer Learning

Section 20: Transfer Learning in R

Lecture 55 Project - Transfer Learning - VGG16 (Implementation)

Lecture 56 Project - Transfer Learning - VGG16 (Performance)

Section 21: Congratulations & about your certificate

Lecture 57 The final milestone!

Lecture 58 Bonus lecture

People pursuing a career in data science,Working Professionals bning their Deep Learning journey,Anyone curious to master image recognition from Bner level in short span of

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