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Overview This comprehensive course on Complete iOS 11 and Swift 4 will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Complete iOS 11 and Swift 4 comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. How will I get my certificate? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is This course for? There is no experience or previous qualifications required for enrolment on this Complete iOS 11 and Swift 4. It is available to all students, of all academic backgrounds. Requirements Our Complete iOS 11 and Swift 4 is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career Path Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to- Open doors of opportunities Increase your adaptability Keep you relevant Boost confidence And much more! Course Curriculum 13 sections • 177 lectures • 19:08:00 total length •iOS 11 Course Overview: 00:11:00 •Install Xcode 9: 00:07:00 •Xcode 9 Beta 4 Update: 00:09:00 •App: Hustle - Your first iOS 11 App: 00:22:00 •Variables, operators, and how computers work: 00:17:00 •Strings in Swift: 00:15:00 •Working with numbers in Swift: 00:20:00 •Swift Functions: 00:23:00 •Booleans: 00:21:00 •Constants: 00:10:00 •Array Data Structure in Swift: 00:13:00 •Swift Loops: 00:19:00 •Dictionary Data Structure in Swift: 00:19:00 •Object Oriented Programming in Swift: 00:12:00 •Inheritance: 00:08:00 •Polymorphism: 00:08:00 •Optionals: 00:21:00 •Enumerations: 00:20:00 •Extensions: Part 1: 00:15:00 •Extensions: Part 2: 00:21:00 •Intro to Protocols, Delegates - Numbers Example: 00:13:00 •Intro to Protocols, Delegates Part 2 - Question Generator: 00:18:00 •Protocols, Delegates - Building Color Magic App UI: 00:17:00 •Protocols, Delegates - Using the Delegate Method in Color Magic App: 00:16:00 •Protocols, Delegates - Using Mutating Functions in Types: 00:18:00 •Git and Version Control - The Fun Way!: 00:12:00 •Terminal Basics - Changing Directories: 00:06:00 •Terminal Basics - Creating Directories and Files: 00:05:00 •Terminal Basics - Copying and Renaming Files: 00:09:00 •Terminal Basics - Deleting Files and Directories: 00:06:00 •Git Basics: 00:17:00 •Setting up Github: 00:05:00 •Working with Local and Remote Repositories: 00:11:00 •Handling Git Merge Conflicts: 00:17:00 •App: Swoosh 01 - Creating the Welcome Screen: 00:25:00 •App: Swoosh 02 - Working with Frames: 00:16:00 •App: Swoosh 03 - Intro to Auto Layout: 00:27:00 •App: Swoosh 04 - Working with Stack Views: 00:27:00 •App: Swoosh 05 - Intro to Segues (Changing Screens): 00:10:00 •App: Swoosh 06 - Refactoring in Xcode 9: 00:10:00 •App: Swoosh 07 - Debugging: setValue forUndefinedKey: 00:04:00 •App: Swoosh 08 - Programmatic Segues: 00:09:00 •App: Swoosh 09 - IBActions (Handling Events) and Data Models: 00:16:00 •App: Swoosh 10 - Passing Data Between View Controllers: 00:12:00 •App: Dev Profile 01 - Auto layout for iPhones: 00:22:00 •App: Dev Profile 02 - Auto layout for iPads (Size Classes): 00:20:00 •App: Window Shopper 01 - Custom Text Fields: 00:18:00 •App: Window Shopper 02 - Input Accessory Views: 00:15:00 •App: Window Shopper 03 - Unit Testing our Data: 00:17:00 •App: Window Shopper 04 - Calculation Algorithm: 00:13:00 •App: Window Shopper 05 - Custom Drawing with drawRect: 00:12:00 •App: Coder Swag 01 - Project creation: 00:23:00 •App: Coder Swag 02 - Tableviews, Delegate, and Data Source: 00:33:00 •App: Coder Swag 03 - Collection Views (Grid Layouts): 00:13:00 •App: Coder Swag 04 - Working with Data Models: 00:14:00 •App: Coder Swag 05 - Displaying Data in Collection View Cells: 00:20:00 •Intro to Chat App: 00:04:00 •App: Smack - Project Setup: 00:26:00 •App: Smack - SWReveal: 00:20:00 •App: Smack - ChannelVC UI: 00:25:00 •App: Smack - LoginVC UI: 00:22:00 •App: Smack - CreateAccountVC UI: 00:19:00 •App: Smack - Web request and API: 00:09:00 •App: Smack - Hosting API: 00:20:00 •App: Smack - Locally Hosting API: 00:18:00 •App: Smack - Creating a web request in Xcode: 00:28:00 •App: Smack - Registering a User: 00:16:00 •App: Smack - Logging in a user: 00:20:00 •App: Smack - Creating a user: 00:26:00 •App: Smack - Avatar Picker Part 1: 00:19:00 •App: Smack - Avatar Picker Part 2: 00:20:00 •App: Smack - Generate a Avatar BG Color: 00:26:00 •App: Smack - LoggedIn Interface: 00:23:00 •App: Smack - Profile View: 00:25:00 •App: Smack - Logging in users: 00:23:00 •App: Smack - Getting channels: 00:19:00 •App: Smack - Channels TableView: 00:14:00 •App: Smack - Add Channel VC: 00:19:00 •App: Smack - Sockets and Channels: 00:26:00 •App: Smack - Refining Login Flow: 00:19:00 •App: Smack - Fetching Messages: 00:20:00 •App: Smack - Sending First Message: 00:17:00 •App: Smack - Displaying Chat Messages: 00:18:00 •App: Smack - Sockets and Messages: 00:19:00 •App: Smack - Typing Users: 00:22:00 •App: Smack - Unread Channels: 00:18:00 •Where to go from here: 00:08:00 •I'm Back: 00:08:00 •Intro to App: Pixel City: 00:02:00 •Creating Xcode Project: Pixel City: 00:04:00 •Installing Alamofire / AlamofireImage Cocoapods: 00:07:00 •Building MapVC UI /Conforming to MKMapViewDelegate /Setting Delegate of mapView: 00:15:00 •Requesting Location Services in iOS 11 / Centering Map On User Location: 00:18:00 •Adding UITapGestureRecognizer to Drop Custom Pins on MapView: 00:15:00 •Setting a Custom Map Annotation Color: 00:05:00 •Animating Photos View / Programmatically adding spinner and label subviews: 00:20:00 •Adding UILabel for Pull Up View / Adding UICollectionView Programmatically: 00:17:00 •Getting API Key from Flickr / Using Flickr API URL Format: 00:14:00 •Using Alamofire to Download URLS: 00:21:00 •Using Alamofire to Download Images / Cancelling All Sessions: 00:16:00 •Setting Up UICollectionView / Adding Images / Reloading UICollectionView: 00:10:00 •Building PopVC / Presentation PopVC When UICollectionViewCell is Tapped: 00:16:00 •Adding 3D Touch Peek: 00:15:00 •Challenge 1: 00:02:00 •Setting up developer: 00:10:00 •Implementing Google AdMob: 00:19:00 •Fetching a list of Products: 00:15:00 •Starting an in-app Purchase: 00:09:00 •Testing in-app Purchases: 00:18:00 •Restoring in-app Purchases after App Deletion: 00:09:00 •Intro to App: GoalPost: 00:03:00 •Creating Xcode Project / Project Folders: 00:04:00 •Building GoalsVC: 00:14:00 •Building GoalCell: 00:14:00 •What is Core Data?: 00:06:00 •Creating Goal Core Data Entity and Attributes: 00:08:00 •Displaying Static GoalCells in UITableView / Creating GoalType Enum: 00:10:00 •Building CreateGoalVC: 00:15:00 •Creating a UIViewController Extension: 00:11:00 •Creating a UIView / UIButton Extension: 00:19:00 •Building FinishGoalVC / Passing Data from CreateGoalVC: 00:19:00 •Saving Goal Data to Persistent Store: 00:13:00 •Fixing Dismissal of FinishGoalVC: 00:07:00 •Fetching Data from Persistent Store / Filling UITableView with Fetched Data: 00:16:00 •Removing Objects from Persistent Store using UITableView Delete Action: 00:11:00 •Setting Goal Progress for UITableViewCell: 00:15:00 •Challenge 2: 00:01:00 •Intro to App: Breakpoint: 00:03:00 •Creating Xcode Project / Setting Up Project Folders: 00:05:00 •Creating Firebase Project: 00:11:00 •Setting Up DataService / Creating Firebase Database Users: 00:11:00 •Building AuthVC and LoginVC in Interface Builder: 00:18:00 •Creating InsetTextField and ShadowView Subclasses: 00:18:00 •Setting up AuthService: 00:13:00 •Building FeedVC and GroupsVC in Interface Builder: 00:16:00 •Presenting LoginVC from AppDelegate / Allowing Login with Email: 00:22:00 •Building MeVC and Adding to UITabBarController: 00:10:00 •Creating CreatePostVC and Uploading Posts to Firebase: 00:20:00 •Creating UIView Extension for Binding Views to Keyboard: 00:15:00 •Building FeedCell: 00:10:00 •Writing the Message Model and Getting All Feed Messages from Firebase: 00:21:00 •Converting UIDs into Emails and Reversing the Order of a TableView: 00:20:00 •Creating CreateGroupVC and Connecting @IBOutlets/Actions: 00:15:00 •Creating UserCell: 00:16:00 •Searching for Email Accounts to Add to Group: 00:19:00 •Adding Users to Group with didSelectRowAt indexPath: 00:21:00 •Creating Groups and pushing them to Firebase: 00:16:00 •Creating GroupCell: 00:15:00 •Creating Group Model and Getting All Groups from Firebase: 00:19:00 •Building GroupFeedVC: 00:18:00 •Initializing Group Data for a Group and Presenting on GroupFeedVC: 00:16:00 •Downloading All Message for a Group and Animating Upon New Message: 00:24:00 •Creating a UIViewController Extension for Presenting GroupFeedVC: 00:07:00 •Challenge 3: 00:02:00 •Intro to app: 00:02:00 •Intro to CoreML: 00:05:00 •What is machine learning?: 00:08:00 •Creating Xcode 9 project: 00:03:00 •Building UI: 00:18:00 •AVFoundation: 00:18:00 •Tap gestures to take snapshot on item: 00:11:00 •Core ML Xcode 9 Beta 4 Update / Fix Preview Photo Crash: 00:03:00 •Downloading CoreML models: 00:21:00 •Adding UI controls for flash control: 00:07:00 •Training your app to speak what it sees: 00:18:00 •App: RampUp - Intro to ARKit App: 00:02:00 •App: RampUp - Resources: 00:04:00 •App: RampUp - Project creation: 00:11:00 •App: RampUp - SceneKit, 3D models: 00:11:00 •App: RampUp - Ramp picker popover: 00:14:00 •App: RampUp - 3D models in SceneKit for popover: 00:18:00 •App: RampUp - 3D models in SceneKit for popover part 2: 00:12:00 •App: RampUp - Detecting taps on 3D objects: 00:17:00 •App: RampUp - Placing ramps in ARKit: 00:24:00 •App: RampUp - Moving objects in 3D space in augmented reality: 00:20:00 •ARKit - where to go next: 00:04:00 •Assignment - Complete iOS 11 and Swift 4: 00:00:00

Overview of MySql Masterclass Join our MySql Masterclass course and discover your hidden skills, setting you on a path to success in this area. Get ready to improve your skills and achieve your biggest goals. The MySql Masterclass course has everything you need to get a great start in this sector. Improving and moving forward is key to getting ahead personally. The MySql Masterclass course is designed to teach you the important stuff quickly and well, helping you to get off to a great start in the field. So, what are you looking for? Enrol now! This MySql Masterclass Course will help you to learn: Learn strategies to boost your workplace efficiency. Hone your skills to help you advance your career. Acquire a comprehensive understanding of various topics and tips. Learn in-demand skills that are in high demand among UK employers This course covers the topic you must know to stand against the tough competition. The future is truly yours to seize with this MySql Masterclass. Enrol today and complete the course to achieve a certificate that can change your career forever. Details Perks of Learning with IOMH One-To-One Support from a Dedicated Tutor Throughout Your Course. Study Online — Whenever and Wherever You Want. Instant Digital/ PDF Certificate. 100% Money Back Guarantee. 12 Months Access. Process of Evaluation After studying the course, an MCQ exam or assignment will test your skills and knowledge. You have to get a score of 60% to pass the test and get your certificate. Certificate of Achievement Certificate of Completion – Digital / PDF Certificate After completing the MySql Masterclass course, you can order your CPD Accredited Digital/ PDF Certificate for £5.99. Certificate of Completion – Hard copy Certificate You can get the CPD Accredited Hard Copy Certificate for £12.99. Shipping Charges: Inside the UK: £3.99 International: £10.99 Who Is This Course for? This MySql Masterclass is suitable for anyone aspiring to start a career in relevant field; even if you are new to this and have no prior knowledge, this course is going to be very easy for you to understand. On the other hand, if you are already working in this sector, this course will be a great source of knowledge for you to improve your existing skills and take them to the next level. This course has been developed with maximum flexibility and accessibility, making it ideal for people who don’t have the time to devote to traditional education. Requirements You don’t need any educational qualification or experience to enrol in the MySql Masterclass course. Do note: you must be at least 16 years old to enrol. Any internet-connected device, such as a computer, tablet, or smartphone, can access this online course. Career Path The certification and skills you get from this MySql Masterclass Course can help you advance your career and gain expertise in several fields, allowing you to apply for high-paying jobs in related sectors.

Overview This Deep Learning & Neural Networks Python - Keras: For Dummies course will unlock your full potential and will show you how to excel in a career in Deep Learning & Neural Networks Python - Keras: For Dummies. So upskill now and reach your full potential. Everything you need to get started in Deep Learning & Neural Networks Python - Keras: For Dummies is available in this course. Learning and progressing are the hallmarks of personal development. This Deep Learning & Neural Networks Python - Keras: For Dummies will quickly teach you the must-have skills needed to start in the relevant industry. In This Deep Learning & Neural Networks Python - Keras: For Dummies Course, You Will: Learn strategies to boost your workplace efficiency. Hone your Deep Learning & Neural Networks Python - Keras: For Dummies skills to help you advance your career. Acquire a comprehensive understanding of various Deep Learning & Neural Networks Python - Keras: For Dummies topics and tips from industry experts. Learn in-demand Deep Learning & Neural Networks Python - Keras: For Dummies skills that are in high demand among UK employers, which will help you to kickstart your career. This Deep Learning & Neural Networks Python - Keras: For Dummies course covers everything you must know to stand against the tough competition in the Deep Learning & Neural Networks Python - Keras: For Dummies field.  The future is truly yours to seize with this Deep Learning & Neural Networks Python - Keras: For Dummies. Enrol today and complete the course to achieve a Deep Learning & Neural Networks Python - Keras: For Dummies certificate that can change your professional career forever. Additional Perks of Buying a Course From Institute of Mental Health Study online - whenever and wherever you want. One-to-one support from a dedicated tutor throughout your course. Certificate immediately upon course completion 100% Money back guarantee Exclusive discounts on your next course purchase from Institute of Mental Health Enrolling in the Deep Learning & Neural Networks Python - Keras: For Dummies course can assist you in getting into your desired career quicker than you ever imagined. So without further ado, start now. Process of Evaluation After studying the Deep Learning & Neural Networks Python - Keras: For Dummies course, your skills and knowledge will be tested with a MCQ exam or assignment. You must get a score of 60% to pass the test and get your certificate.  Certificate of Achievement Upon successfully completing the Deep Learning & Neural Networks Python - Keras: For Dummies course, you will get your CPD accredited digital certificate immediately. And you can also claim the hardcopy certificate completely free of charge. All you have to do is pay a shipping charge of just £3.99. Who Is This Course for? This Deep Learning & Neural Networks Python - Keras: For Dummies is suitable for anyone aspiring to start a career in Deep Learning & Neural Networks Python - Keras: For Dummies; even if you are new to this and have no prior knowledge on Deep Learning & Neural Networks Python - Keras: For Dummies, this course is going to be very easy for you to understand.  And if you are already working in the Deep Learning & Neural Networks Python - Keras: For Dummies field, this course will be a great source of knowledge for you to improve your existing skills and take them to the next level.  Taking this Deep Learning & Neural Networks Python - Keras: For Dummies course is a win-win for you in all aspects.  This course has been developed with maximum flexibility and accessibility, making it ideal for people who don't have the time to devote to traditional education. Requirements This Deep Learning & Neural Networks Python - Keras: For Dummies course has no prerequisite.  You don't need any educational qualification or experience to enrol in the Deep Learning & Neural Networks Python - Keras: For Dummies course. Do note: you must be at least 16 years old to enrol. Any internet-connected device, such as a computer, tablet, or smartphone, can access this online Deep Learning & Neural Networks Python - Keras: For Dummies course. Moreover, this course allows you to learn at your own pace while developing transferable and marketable skills. Course Curriculum Course Introduction and Table of Contents Course Introduction and Table of Contents 00:11:00 Deep Learning Overview Deep Learning Overview - Theory Session - Part 1 00:06:00 Deep Learning Overview - Theory Session - Part 2 00:07:00 Choosing Between ML or DL for the next AI project - Quick Theory Session Choosing Between ML or DL for the next AI project - Quick Theory Session 00:09:00 Preparing Your Computer Preparing Your Computer - Part 1 00:07:00 Preparing Your Computer - Part 2 00:06:00 Python Basics Python Basics - Assignment 00:09:00 Python Basics - Flow Control 00:09:00 Python Basics - Functions 00:04:00 Python Basics - Data Structures 00:12:00 Theano Library Installation and Sample Program to Test Theano Library Installation and Sample Program to Test 00:11:00 TensorFlow library Installation and Sample Program to Test TensorFlow library Installation and Sample Program to Test 00:09:00 Keras Installation and Switching Theano and TensorFlow Backends Keras Installation and Switching Theano and TensorFlow Backends 00:10:00 Explaining Multi-Layer Perceptron Concepts Explaining Multi-Layer Perceptron Concepts 00:03:00 Explaining Neural Networks Steps and Terminology Explaining Neural Networks Steps and Terminology 00:10:00 First Neural Network with Keras - Understanding Pima Indian Diabetes Dataset First Neural Network with Keras - Understanding Pima Indian Diabetes Dataset 00:07:00 Explaining Training and Evaluation Concepts Explaining Training and Evaluation Concepts 00:11:00 Pima Indian Model - Steps Explained Pima Indian Model - Steps Explained - Part 1 00:09:00 Pima Indian Model - Steps Explained - Part 2 00:07:00 Coding the Pima Indian Model Coding the Pima Indian Model - Part 1 00:11:00 Coding the Pima Indian Model - Part 2 00:09:00 Pima Indian Model - Performance Evaluation Pima Indian Model - Performance Evaluation - Automatic Verification 00:06:00 Pima Indian Model - Performance Evaluation - Manual Verification 00:08:00 Pima Indian Model - Performance Evaluation - k-fold Validation - Keras Pima Indian Model - Performance Evaluation - k-fold Validation - Keras 00:10:00 Pima Indian Model - Performance Evaluation - Hyper Parameters Pima Indian Model - Performance Evaluation - Hyper Parameters 00:12:00 Understanding Iris Flower Multi-Class Dataset Understanding Iris Flower Multi-Class Dataset 00:08:00 Developing the Iris Flower Multi-Class Model Developing the Iris Flower Multi-Class Model - Part 1 00:09:00 Developing the Iris Flower Multi-Class Model - Part 2 00:06:00 Developing the Iris Flower Multi-Class Model - Part 3 00:09:00 Understanding the Sonar Returns Dataset Understanding the Sonar Returns Dataset 00:07:00 Developing the Sonar Returns Model Developing the Sonar Returns Model 00:10:00 Sonar Performance Improvement - Data Preparation - Standardization Sonar Performance Improvement - Data Preparation - Standardization 00:15:00 Sonar Performance Improvement - Layer Tuning for Smaller Network Sonar Performance Improvement - Layer Tuning for Smaller Network 00:07:00 Sonar Performance Improvement - Layer Tuning for Larger Network Sonar Performance Improvement - Layer Tuning for Larger Network 00:06:00 Understanding the Boston Housing Regression Dataset Understanding the Boston Housing Regression Dataset 00:07:00 Developing the Boston Housing Baseline Model Developing the Boston Housing Baseline Model 00:08:00 Boston Performance Improvement by Standardization Boston Performance Improvement by Standardization 00:07:00 Boston Performance Improvement by Deeper Network Tuning Boston Performance Improvement by Deeper Network Tuning 00:05:00 Boston Performance Improvement by Wider Network Tuning Boston Performance Improvement by Wider Network Tuning 00:04:00 Save & Load the Trained Model as JSON File (Pima Indian Dataset) Save & Load the Trained Model as JSON File (Pima Indian Dataset) - Part 1 00:09:00 Save & Load the Trained Model as JSON File (Pima Indian Dataset) - Part 2 00:08:00 Save and Load Model as YAML File - Pima Indian Dataset Save and Load Model as YAML File - Pima Indian Dataset 00:05:00 Load and Predict using the Pima Indian Diabetes Model Load and Predict using the Pima Indian Diabetes Model 00:09:00 Load and Predict using the Iris Flower Multi-Class Model Load and Predict using the Iris Flower Multi-Class Model 00:08:00 Load and Predict using the Sonar Returns Model Load and Predict using the Sonar Returns Model 00:10:00 Load and Predict using the Boston Housing Regression Model Load and Predict using the Boston Housing Regression Model 00:08:00 An Introduction to Checkpointing An Introduction to Checkpointing 00:06:00 Checkpoint Neural Network Model Improvements Checkpoint Neural Network Model Improvements 00:10:00 Checkpoint Neural Network Best Model Checkpoint Neural Network Best Model 00:04:00 Loading the Saved Checkpoint Loading the Saved Checkpoint 00:05:00 Plotting Model Behavior History Plotting Model Behavior History - Introduction 00:06:00 Plotting Model Behavior History - Coding 00:08:00 Dropout Regularization - Visible Layer Dropout Regularization - Visible Layer - Part 1 00:11:00 Dropout Regularization - Visible Layer - Part 2 00:06:00 Dropout Regularization - Hidden Layer Dropout Regularization - Hidden Layer 00:06:00 Learning Rate Schedule using Ionosphere Dataset - Intro Learning Rate Schedule using Ionosphere Dataset 00:06:00 Time Based Learning Rate Schedule Time Based Learning Rate Schedule - Part 1 00:07:00 Time Based Learning Rate Schedule - Part 2 00:12:00 Drop Based Learning Rate Schedule Drop Based Learning Rate Schedule - Part 1 00:07:00 Drop Based Learning Rate Schedule - Part 2 00:08:00 Convolutional Neural Networks - Introduction Convolutional Neural Networks - Part 1 00:11:00 Convolutional Neural Networks - Part 2 00:06:00 MNIST Handwritten Digit Recognition Dataset Introduction to MNIST Handwritten Digit Recognition Dataset 00:06:00 Downloading and Testing MNIST Handwritten Digit Recognition Dataset 00:10:00 MNIST Multi-Layer Perceptron Model Development MNIST Multi-Layer Perceptron Model Development - Part 1 00:11:00 MNIST Multi-Layer Perceptron Model Development - Part 2 00:06:00 Convolutional Neural Network Model using MNIST Convolutional Neural Network Model using MNIST - Part 1 00:13:00 Convolutional Neural Network Model using MNIST - Part 2 00:12:00 Large CNN using MNIST Large CNN using MNIST 00:09:00 Load and Predict using the MNIST CNN Model Load and Predict using the MNIST CNN Model 00:14:00 Introduction to Image Augmentation using Keras Introduction to Image Augmentation using Keras 00:11:00 Augmentation using Sample Wise Standardization Augmentation using Sample Wise Standardization 00:10:00 Augmentation using Feature Wise Standardization & ZCA Whitening Augmentation using Feature Wise Standardization & ZCA Whitening 00:04:00 Augmentation using Rotation and Flipping Augmentation using Rotation and Flipping 00:04:00 Saving Augmentation Saving Augmentation 00:05:00 CIFAR-10 Object Recognition Dataset - Understanding and Loading CIFAR-10 Object Recognition Dataset - Understanding and Loading 00:12:00 Simple CNN using CIFAR-10 Dataset Simple CNN using CIFAR-10 Dataset - Part 1 00:09:00 Simple CNN using CIFAR-10 Dataset - Part 2 00:06:00 Simple CNN using CIFAR-10 Dataset - Part 3 00:08:00 Train and Save CIFAR-10 Model Train and Save CIFAR-10 Model 00:08:00 Load and Predict using CIFAR-10 CNN Model Load and Predict using CIFAR-10 CNN Model 00:16:00 RECOMENDED READINGS Recomended Readings 00:00:00

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Course Curriculum Introduction Introduction to Maven 00:01:00 Topics Covered in this Course 00:03:00 How Maven Helps to a DevOps Engineer 00:02:00 Resource to Start 00:01:00 Setup Maven Environment on Windows Setup Maven Environment on Windows 00:01:00 Java Installation on Windows 00:05:00 Eclipse Installation 00:03:00 Create 1st Maven Project 00:04:00 Maven Coordinates 00:07:00 Default Directory Structure 00:03:00 Steps Involved to Build a Java Project 00:02:00 Maven Goals 00:06:00 Maven Goals in Action 00:07:00 Maven Goals, Repositories What is pom.xml and How it Works 00:05:00 About the Section 00:02:00 Maven Repositories 00:06:00 Maven goals - Deploy Goal for Local Repository 00:03:00 Push Maven Project onto GitHub 00:08:00 Maven on Linux Create an EC2 Instance 00:04:00 Prerequisites to Setup Maven 00:05:00 Setup Maven 00:04:00 Build Maven Project on Linux 00:10:00 Common Maven Goals 00:03:00 Create a Project with Maven Archetype 00:06:00 Plugins and dependencies in pom.xml 00:09:00 Settings.xml file 00:03:00 Tomcat Server Configurations 00:05:00 Deploy war on Tomcat Server 00:10:00 Maven in DevOps workflow Maven in DevOps workflow 00:01:00 Jenkins Installation 00:07:00 Add Maven Build Server as an Agent to Jenkins 00:07:00 Build Jenkins Job on Slave Node 00:07:00 Build a war file on Agent Node (Maven Server) 00:04:00 Additional Resources Create an AWS account 00:06:00 Install Git bash 00:03:00 Connect to a Linux with Mobaxterm 00:04:00 Install Tomcat 00:11:00 SonarQube SonarQube Introduction 00:07:00 SonarQube Installation 00:11:00 SonarQube Console Overview 00:14:00 SonarQube Installation with PostgreSQL Database 00:29:00 SonarQube Integration with Jenkins 00:15:00

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