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
Master the OpenGL and GLSL shading language - by writing code! Illustrated theory and practice (from basics to advanced)
This course covers all the basics to more advanced features and dives into all the little details about Hasura. Hasura provides a platform to create your GraphQL backends easier and faster than you ever thought possible without writing a single line of code!
Python Certification - Beginner to Expert offers a comprehensive journey from novice to proficient Python programmer. This course is structured into 18 sections, covering essential topics such as software installation, conditional branching, file handling, and database management.
Duration 4 Days 24 CPD hours This course is intended for This is an intermediate -level programming course, designed for experienced Java developers who wish to get up and running on developing well defended software applications. Familiarity with Java and JEE is required and real world programming experience is highly recommended. Ideally students should have approximately 6 months to a year of Java and JEE working knowledge. Overview Students who attend Attacking and Securing Java Web Applications will leave the course armed with the skills required to recognize actual and potential software vulnerabilities and implement defenses for those vulnerabilities. This course begins by developing the skills required to fingerprint a web application and then scan it for vulnerabilities and bugs. Practical labs using current tools and techniques provide students with the experience needed to begin testing their own applications. Students also gain a deeper understanding of how attackers probe applications to understand the runtime environment as well as find potential weaknesses. This course the introduces developers to the most common security vulnerabilities faced by web applications today. Each vulnerability is examined from a Java/JEE perspective through a process of describing the threat and attack mechanisms, recognizing associated vulnerabilities, and, finally, designing, implementing, and testing effective defenses. Practical labs reinforce these concepts with real vulnerabilities and attacks. Students are then challenged to design and implement the layered defenses they will need in defending their own applications. There is an emphasis on the underlying vulnerability patterns since the technologies, use cases, and methods of attack as constantly changing. The patterns remain the same through all the change and flux. This 'skills-centric' course is about 50% hands-on lab and 50% lecture, designed to train attendees in secure web application development, coding and design, coupling the most current, effective techniques with the soundest industry practices. Our engaging instructors and mentors are highly experienced practitioners who bring years of current 'on-the-job' experience into every classroom. This lab-intensive course provides hands-on Java / JEE security training that offers a unique look at Java application security. Beginning with penetration testing and hunting for bugs in Java web applications, you embrace best practices for defensively coding web applications, covering all the OWASP Top Ten as well as several additional prominent vulnerabilities. You will repeatedly attack and then defend various assets associated with fully functional web applications and services, allowing you to experience the mechanics of how to secure JEE web applications in the most practical of terms. Bug Hunting Foundation Why Hunt Bugs? Safe and Appropriate Bug Hunting/Hacking Scanning Web Applications Scanning Applications Overview Moving Forward from Hunting Bugs Removing Bugs Foundation for Securing Applications Principles of Information Security Bug Stomping 101 Unvalidated Data Broken Authentication Sensitive Data Exposure XML External Entities (XXE) Broken Access Control Bug Stomping 102 Security Misconfiguration Cross Site Scripting (XSS) Deserialization/Vulnerable Components Insufficient Logging and Monitoring Spoofing, CSRF, and Redirects Moving Forward with Application Security Applications: What Next? Making Application Security Real
Duration 4 Days 24 CPD hours This course is intended for This is an intermediate-level programming course, designed for experienced .Net developers who wish to get up and running on developing well defended software applications. Real world programming experience with .Net is required. Overview Students who attend Attacking and Securing .Net Web Applications will leave the course armed with the skills required to recognize actual and potential software vulnerabilities and implement defenses for those vulnerabilities. This course begins by developing the skills required to fingerprint a web application and then scan it for vulnerabilities and bugs. Practical labs using current tools and techniques provide students with the experience needed to begin testing their own applications. Students also gain a deeper understanding of how attackers probe applications to understand the runtime environment as well as find potential weaknesses. This course the introduces developers to the most common security vulnerabilities faced by web applications today. Each vulnerability is examined from a .Net perspective through a process of describing the threat and attack mechanisms, recognizing associated vulnerabilities, and, finally, designing, implementing effective defenses. Practical labs reinforce these concepts with real vulnerabilities and attacks. Students are then challenged to design and implement the layered defenses they will need in defending their own applications. There is an emphasis on the underlying vulnerability patterns since the technologies, use cases, and methods of attack as constantly changing. The patterns remain the same through all the change and flux. This 'skills-centric' course is about 50% hands-on lab and 50% lecture, designed to train attendees in secure web application development, coding and design, coupling the most current, effective techniques with the soundest industry practices. Our instructors and mentors are highly experienced practitioners who bring years of current 'on-the-job' experience into every classroom. This lab-intensive course provides hands-on .Net security training that offers a unique look at .Net application security. Beginning with penetration testing and hunting for bugs in .Net web applications, you thoroughly examine best practices for defensively coding web applications, covering all the OWASP Top Ten as well as several additional prominent vulnerabilities. You will repeatedly attack and then defend various assets associated with fully functional web applications and services, driving home the mechanics of how to secure .Net web applications in the most practical of terms. Bug Hunting Foundation Why Hunt Bugs? Safe and Appropriate Bug Hunting/Hacking Scanning Web Applications Scanning Applications Overview Moving Forward from Hunting Bugs Removing Bugs Foundation for Securing Applications Principles of Information Security Bug Stomping 101 Unvalidated Data Injection Broken Authentication Sensitive Data Exposure XML External Entities (XXE) Broken Access Control Bug Stomping 102 Security Misconfiguration Cross Site Scripting (XSS) Deserialization/Vulnerable Components Insufficient Logging and Monitoring Spoofing, CSRF, and Redirects Moving Forward with Application Security Applications: What Next? .NET Issues and Best Practices Making Application Security Real Time Permitting Topics Cryptography Overview .NET Cryptographic Services
VueJS has rapidly gained popularity as one of the most widely used JavaScript frameworks, known for its approachability. In this course, you will delve into the fundamentals of VueJS, including its setup process and the underlying principles that make it work. Through practical application, you will reinforce your understanding by developing a practice project, specifically designed for this course. Prior knowledge of basic JavaScript, as well as familiarity with HTML and CSS, is recommended.
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