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Regular expressions training course description Regular expressions are an extremely powerful tool for manipulating text and data. They are now standard features in a wide range of languages and popular tools, including Python and MySQL. Regular expressions allow you to code complex and subtle text processing that you never imagined could be automated. Once you've mastered regular expressions, they'll become an invaluable part of your toolkit. You will wonder how you ever got by without them. What will you learn Use Regular Expressions. Troubleshoot Regular Expressions. Compare RE features among different versions. Explain how the regular expression engine works. Optimize REs. Match what you want, not what you don't want. Regular expressions training course details Who will benefit: Anyone looking to use regular expressions. Prerequisites: None. Duration 1 day Regular expressions training course contents Introduction to Regular Expressions Solving real problems, REs as a language, the filename analogy, language analogy, RE frame of mind, searching text files: egrep, egrep metacharacters, start and end of the line, character classes, matching any character with dot, alternation, ignoring differences in capitalization, word boundaries, optional items, other quantifiers: repetition, parentheses and backreferences, the great escape, expanding the foundation, linguistic diversification, the goal of a RE, more examples, RE nomenclature, Improving on the status quo. Extended introductory examples A short introduction to Perl, matching text with regular expressions, toward a more real-world example, side effects of a successful match, Intertwined regular expression, intermission, modifying text with regular expressions, example: form letter, example: prettifying a stock price, automated editing, a small mail utility, adding commas to a number with lookaround, text-to-HTML conversion, that doubled-word thing. Regular expression features and flavours The regex landscape, origins of REs, care and handling of REs, Integrated handling, procedural and object-oriented handling, search-and-replace example. strings character encodings and modes, strings as REs, character-encoding issues, unicode, regex modes and match modes, common metacharacters and features, character representations, character classes and class-like constructs, anchors and other 'zero-width assertions', comments and mode modifiers, grouping capturing conditionals and control. The mechanics of expression processing Two kinds of engines, new standards, regex engine types, from the department of redundancy department, testing the engine type, match basics, about the examples, rule 1: the match that begins earliest wins, engine pieces and parts, rule 2: the standard quantifiers are greedy, regex-directed versus text-directed, NFA engine: regex-directed, DFA engine: text-directed, first thoughts: NFA and DFA in comparison, backtracking, two important points on backtracking, saved states, backtracking and greediness, more about greediness and backtracking, problems of greediness, multi-character 'quotes', lazy quantifiers, greediness and laziness, laziness and backtracking, possessive quantifiers and atomic grouping, possessive quantifiers ?, +, *+, ++ and {m,n}+, the backtracking of lookaround, is alternation greedy? taking advantage of ordered alternation, NFA DFA and posix, the longest-leftmost', posix and the longest-leftmost rule, speed and efficiency. Practical regex techniques Continuation lines, matching an IP address, working with filenames, matching balanced sets of parentheses, watching out for unwanted matches, matching delimited text, knowing your data and making assumptions, stripping leading and trailing whitespace, matching and HTML tag, matching an HTML link, examining an HTTP URL, validating a hostname, plucking a hostname, plucking a URL, parsing CSV files. Crafting an efficient expression Efficiency vs. correctness, localizing greediness, global view of backtracking, more work for POSIX NFA, work required during a non-match, being more specific, alternation can be expensive, benchmarking, know what you re measuring, benchmarking with Python, common optimisations, the mechanics of regex application, pre-application optimizations, optimizations with the transmission, optimization of the regex itself, techniques for faster expressions, common sense techniques, expose literal text, expose anchors, lazy versus greedy: be specific, split into multiple REs, mimic initial-character discrimination, use atomic grouping and possessive quantifiers, lead the engine to a match, unrolling the loop, observations, using atomic grouping and possessive quantifiers, short unrolling examples, unrolling C comments, the free flowing regex, a helping hand to guide the match, a well-guided regex is a fast regex.

Unlock your true potential with the Reclaiming Your Masculine and Feminine Toolkit! This comprehensive resource offers transformative exercises, including daily self-awareness prompts, boundary-building tools, emotional regulation techniques, and breathwork practices. Balance your energies, enhance self-awareness, and align with your purpose. Perfect for anyone looking to deepen their personal growth journey. Embrace your power today!

Learn Project Benefits Management Step-by-Step A comprehensive process that covers every stage of Project and Program Benefits Management

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

Ready to break free from emotional baggage and embrace inner peace? Our course is designed for those looking for quick, effective ways to release their anger. Join us on a journey of cathartic release and emotional healing.

NodeJS allows you to build complex and powerful applications quickly and easily, writing JavaScript code. It also allows you to use JavaScript for web applications with flexibility for a wide range of different purposes. Learn about MongoDB as a database and how to build it as well as Express as a framework to build web apps on top of Node.js.

Stained Glass Flower Cutting For Mosaic Workshop. Beginners and Intermediates. Learn to cut and shape flowers in stained glass for glass mosaic.

The course 'Deep Learning & Neural Networks Python - Keras' provides a comprehensive introduction to deep learning using the Keras library in Python. It covers topics ranging from basic neural networks to more advanced concepts, such as convolutional neural networks, image augmentation, and performance improvement techniques for various datasets. Learning Outcomes: Understand the fundamental concepts of deep learning and how it differs from traditional machine learning. Gain proficiency in using Keras, a powerful deep learning library, for building and training neural network models. Develop practical skills in creating and optimizing neural network models for different datasets, including image recognition tasks and regression problems. Why buy this Deep Learning & Neural Networks Python - Keras? Unlimited access to the course for forever Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the Deep Learning & Neural Networks Python - Keras there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Who is this course for? This Deep Learning & Neural Networks Python - Keras course is ideal for Students Recent graduates Job Seekers Anyone interested in this topic People already working in the relevant fields and want to polish their knowledge and skill. Prerequisites This Deep Learning & Neural Networks Python - Keras does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Deep Learning & Neural Networks Python - Keras was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Career path As this course comes with multiple courses included as bonus, you will be able to pursue multiple occupations. This Deep Learning & Neural Networks Python - Keras is a great way for you to gain multiple skills from the comfort of your home. 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

BGP training course description A study of BGP for non engineers working in the Internet. The course starts with a review of the basics of routers and routing tables and then moves on to a simple overview of how BPG works with a focus on BGP metrics influencing the route traffic takes through the Internet. Hands on with routers follow the major sessions to reinforce the theory. Note these hands on sessions are more demonstrations by the trainer but some can be followed along and done by delegates (e.g. looking at Internet routing tables.) What will you learn Explain how routing tables influence Internet traffic. Describe how BGP works. Explain the methods BGP can use to influence Internet traffic. Use traceroute, peeringdb, route collectors and looking glasses to analyse traffic flows. Explain the difference between bi lateral and multilateral peering using a route server. BGP training course details Who will benefit: Non technical staff wishing to know more about BGP. Prerequisites: None. Duration 1 day BGP training course contents Networks, routers and routing tables What is a network, what is a router, routing tables, static routes, routing protocols. When an ISP uses static routes and when they use BGP. IP addresses, subnet masks, groups of IP addresses. IPv6. Hands on: Showing a full routing table. Seeing traceroute being used. Basic BGP What's BGP? BGP versus other routing protocols, ASs, AS numbers. RIPE database, peeringdb. Hands on: Finding AS numbers. Showing simple BGP configuration and routing tables in an EVENG example. How BGP works Simple walk through of BGP incremental updates and how routes change when links go down. Hands on: Showing packets and route changes when a link goes down/comes up. BGP path selection Transit, peering, routing policy and route filtering. Longest matching rule in routing tables, route selection order, Local preference, AS prepend, MEDs. Hands on: Seeing BGP influencing traffic. Looking at peering policies in RIPE and peeringdb. Route servers What are route servers? LINX route servers, route server policy control and communities, What are route collectors, Looking glasses. Hands on: Seeing the LINX route server details in peeringdb, using a looking glass.

3 QLS Endorsed Diploma | QLS Hard Copy Certificate Included | 10 CPD Courses | Lifetime Access | 24/7 Tutor Support