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Back propagation fundamentals

Track :

Computer Science

Lessons no : 30

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What will you learn in this course?
  • Master back propagation algorithms for neural network training and optimization techniques including gradient descent and regularization
  • Implement back propagation in Python to develop, train, and fine-tune neural networks for machine learning tasks
  • Apply mathematical concepts like the chain rule and derivatives to improve neural network learning efficiency and accuracy
  • Utilize advanced optimization strategies such as momentum and learning rate schedules to enhance neural network performance
  • Analyze error propagation and weight adjustments to optimize neural network convergence and reduce training time
  • Design neural network architectures with effective back propagation techniques for complex data modeling and pattern recognition
  • Evaluate the impact of regularization methods on preventing overfitting during neural network training
  • Troubleshoot common issues in back propagation, including vanishing gradients and slow convergence, for robust model development
  • Integrate back propagation algorithms with Python libraries like TensorFlow or PyTorch for scalable machine learning solutions
  • Develop practical skills to implement, optimize, and deploy neural networks in real-world machine learning projects

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Lessons | 30


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Back propagation course, in this course we will delve deep into the intricacies of back propagation, understanding its principles, and mastering its application. We will explore how back propagation enables neural networks to learn from data by iteratively adjusting weights to minimize error. Through a series of modules, you will learn the mathematical foundations of back propagation, including the chain rule and gradient descent optimization. We will cover advanced techniques for optimizing back propagation, such as momentum, learning rate schedules, and regularization. Additionally, you will gain practical experience implementing back propagation algorithms in Python, allowing you to train and fine-tune neural networks effectively. Whether you're new to neural networks or seeking to enhance your understanding of back propagation, this course will equip you with the knowledge and skills needed to tackle complex machine learning tasks with confidence.