Tag: Neural Networks

ISTQB AI Testing – Learn best practices and prepare for exam

Posted on by admin

Course content

  • Introduction to A1
  • Quality Characteristics for A1-Based Systems
  • Machine Learning (ML) — Overview
  • ML – Data
  • ML Functional Performance Metrics
  • ML — Neural Networks and Testing
  • Testing A1-Based Systems Overview
  • Testing A1-Specific Quality Characteristics
  • Methods and Techniques for the Testing of A1-Based Systems
  • Test Environments for A1-Based Systems
  • Using A1 for Testing

MQL5 ADVANCED:Code Machine learning EAs with Neural Networks

Posted on by admin

Course content

  • Developing the RSI OBOS EA
  • Introduction to machine learning
  • Coding a Neural Network into the RSI OBOS strategy
  • Cost Averaging trade management
  • Continuous neural network training
  • Conclusion

From Zero to AI Hero: Create Neural Networks with TensorFlow

Posted on by admin

Course content

  • Introduction
  • Why learn TenserFlow
  • Setting up the TensorFlow Environment
  • A1 and Machine Learning Concepts
  • Applying the Machine Learning Workflow with TensorFlow
  • Understanding Neural Networks
  • Building and Training Your First Neural Network
  • Monitoring and Improving Neural Network Performance
  • Deploying Your Neural Network
  • Assignment
  • Conclusion and Final Words

The Complete Self-Driving Car Course – Applied Deep Learning

Posted on by admin

Course content

  • Introduction
  • Installation
  • Python Crash Course (Optional)
  • NumPy Crash Course (Optional)
  • Computer Vision: Finding Lane Lines
  • The Perceptron
  • Keras
  • Deep Neural Networks
  • Multiclass Classification
  • MN 1ST Image Recognition
  • Convolutional Neural Networks
  • Classifying Road Symbols
  • Polynomial Regression
  • Behavioural Cloning

Deep Learning: Convolutional Neural Networks in Python

Posted on by admin

Course content

  • Welcome
  • Google Colab
  • Machine Learning and Neurons
  • Feedforward Artificial Neural Networks
  • Convolutional Neural Networks
  • Natural Language Processing (NLP)
  • Convolution In-Depth
  • Convolutional Neural Network Description
  • Practical Tips
  • In-Depth: Loss Functions
  • In-Depth: Gradient Descent
  • Setting Up Your Environment (FAQ by Student Request)
  • Extra Help With Python Coding for Beginners (FAQ by Student Request)
  • Effective Learning Strategies for Machine Learning (FAQ by Student Request)
  • Appendix / FAQ Finale

The Data Science Course: Complete Data Science Bootcamp 2024

Posted on by admin

Course content

  • Part 1: Introduction
  • The Field of Data Science – The Various Data Science Disciplines
  • The Field of Data Science – Connecting the Data Science Disciplines
  • The Field of Data Science – The Benefits of Each Discipline
  • The Field of Data Science – Popular Data Science Techniques
  • The Field of Data Science – Popular Data Science Tools
  • The Field of Data Science – Careers in Data Science
  • The Field of Data Science – Debunking Common Misconceptions
  • Part 2: Probability
  • Probability – Combinatorics
  • Probability – Bayesian Inference
  • Probability – Distributions
  • Probability – Probability in Other Fields
  • Part 3: Statistics
  • Statistics – Descriptive Statistics
  • Statistics – Practical Example: Descriptive Statistics
  • Statistics – Inferential Statistics Fundamentals
  • Statistics – Inferential Statistics: Confidence Intervals
  • Statistics – Practical Example: Inferential Statistics
  • Statistics – Hypothesis Testing
  • Statistics – Practical Example: Hypothesis Testing
  • Part 4: Introduction to Python
  • Python – Variables and Data Types
  • Python – Basic Python Syntax
  • Python – Other Python Operators
  • Python – Conditional Statements
  • Python – Python Functions
  • Python – Sequences
  • Python – Iterations
  • Python – Advanced Python Tools
  • Part 5: Advanced Statistical Methods in Python
  • Advanced Statistical Methods – Linear Regression with StatsModels
  • Advanced Statistical Methods – Multiple Linear Regression with StatsModels
  • Advanced Statistical Methods – Linear Regression with sklearn
  • Advanced Statistical Methods – Practical Example: Linear Regression
  • Advanced Statistical Methods – Logistic Regression
  • Advanced Statistical Methods – Cluster Analysis
  • Advanced Statistical Methods – K-Means Clustering
  • Advanced Statistical Methods – Other Types of Clustering
  • Part 6: Mathematics
  • Part 7: Deep Learning
  • Deep Learning – Introduction to Neural Networks
  • Deep Learning – How to Build a Neural Network from Scratch with NumPy
  • Deep Learning – TensorFlow 2.0: Introduction
  • Deep Learning – Digging Deeper into NNs: Introducing Deep Neural Networks
  • Deep Learning – Overfitting
  • Deep Learning – Initialization
  • Deep Learning – Digging into Gradient Descent and Learning Rate Schedules
  • Deep Learning – Preprocessing
  • Deep Learning – Classifying on the MNIST Dataset
  • Deep Learning – Business Case Example
  • Deep Learning – Conclusion
  • Appendix: Deep Learning – TensorFlow 1: Introduction
  • Appendix: Deep Learning – TensorFlow 1: Classifying on the MN 1ST Dataset
  • Appendix: Deep Learning – TensorFlow 1: Business Case
  • Software Integration
  • Case Study – What’s Next in the Course?
  • Case Study – Preprocessing the ‘Absenteeism_data’
  • Case Study – Applying Machine Learning to Create the ‘absenteeism module’
  • Case Study – Loading the ‘absenteeism _ module’
  • Case Study – Analyzing the Predicted Outputs in Tableau
  • Appendix – Additional Python Tools
  • Appendix – pandas Fundamentals
  • Appendix – Working with Text Files in Python
  • Bonus Lecture