Johns Hopkins University
Mastering Neural Networks and Model Regularization

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Johns Hopkins University

Mastering Neural Networks and Model Regularization

Erhan Guven

Instructor: Erhan Guven

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

16 hours to complete
3 weeks at 5 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

16 hours to complete
3 weeks at 5 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Build neural networks from scratch and apply them to real-world datasets like MNIST.

  • Apply back-propagation for optimizing neural network models and understand computational graphs.

  • Utilize L1, L2, drop-out regularization, and decision tree pruning to reduce model overfitting.

  • Implement convolutional neural networks (CNNs) and tensors using PyTorch for image and audio processing.

Details to know

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Recently updated!

September 2024

Assessments

12 assignments

Taught in English

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This course is part of the Applied Machine Learning Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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  • Gain a foundational understanding of a subject or tool
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There are 5 modules in this course

This course provides a comprehensive introduction to neural networks, focusing on the perceptron model, regularization techniques, and practical implementation using PyTorch. Students will build and evaluate neural networks, including convolutional architectures for image processing and audio signal modeling. Emphasis will be placed on comparing performance metrics and understanding advanced concepts like computational graphs and loss functions. By the end of the course, participants will be equipped with the skills to effectively design, implement, and optimize neural network models.

What's included

2 readings

In this module, you will learn about the fundamental concepts in neural networks, covering the perceptron model, model parameters, and the back-propagation algorithm. You'll also learn to implement a neural network from scratch and apply it to classify MNIST images, evaluating performance against sklearn's library function.

What's included

4 videos2 readings3 assignments1 ungraded lab

In this module, you'll delve into techniques to enhance machine learning model performance and generalization. You'll grasp the necessity of regularization to mitigate overfitting, compare L1 and L2 regularization methods, understand decision tree pruning, explore dropout regularization in neural networks, and observe how regularization shapes model decision boundaries.

What's included

3 videos3 readings3 assignments1 ungraded lab

In this module, you'll cover essential concepts and practical skills in deep learning using PyTorch. You'll also learn computational graphs in supervised learning, create and manipulate tensors in PyTorch, compare activation and loss functions, learn implementation steps and library functions for neural network training, and optimize models by running them on GPU for enhanced performance.

What's included

3 videos2 readings3 assignments1 ungraded lab

In this module, you'll focus on advanced applications of convolutional neural networks (CNNs) using PyTorch. You'll also learn to implement CNN filters, compare different CNN architectures, develop models for image processing tasks in PyTorch, and explore techniques for modeling audio time signals using Spectrogram features for enhanced analysis and classification.

What's included

2 videos3 readings3 assignments1 programming assignment

Instructor

Erhan Guven
Johns Hopkins University
3 Courses232 learners

Offered by

Recommended if you're interested in Machine Learning

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