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This course is part of Foundations of Neural Networks Specialization
Instructor: Zerotti Woods
Included with
Recommended experience
Intermediate level
A background in basic mathematics, linear algebra, introductory programming and familiarity with machine learning principles is recommended.
Recommended experience
Intermediate level
A background in basic mathematics, linear algebra, introductory programming and familiarity with machine learning principles is recommended.
Understand the foundational mathematics and key concepts driving neural networks and machine learning.
Analyze and apply machine learning algorithms, optimization methods, and loss functions to train and evaluate models effectively.
Explore the design and structure of feedforward neural networks, using gradient descent to optimize and train deep models.
Investigate convolutional neural networks, their elements, and how they apply to real-world problems like image processing and computer vision.
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December 2024
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The course "Introduction to Neural Networks" provides a comprehensive introduction to the foundational concepts of neural networks, equipping learners with essential skills in deep learning and machine learning. Dive into the mathematics that drive neural network algorithms and explore the optimization techniques that enhance their performance. Gain hands-on experience training machine learning models using gradient descent and evaluate their effectiveness in practical scenarios.
You’ll also delve into the architecture of feedforward neural networks and the innovative techniques used to prevent overfitting, such as dropout and regularization. The course uniquely emphasizes Convolutional Neural Networks (CNNs), highlighting their applications in fields like computer vision and image processing. Real-world examples and research insights will help you stay current with advancements in neural networks while preparing you to propose innovative solutions for emerging challenges. This course offers the tools and knowledge to advance your expertise in algorithms and machine learning methodologies.
This course provides a comprehensive overview of the foundational mathematics and concepts behind Deep Learning and Machine Learning. Students will analyze various Machine Learning Algorithms, focusing on Optimization Techniques and Regularization Methods, while evaluating their effectiveness. Practical applications will include training algorithms using Gradient Descent and assessing their performance. The course also covers the structure and data elements of Convolutional Neural Networks (CNNs), emphasizing their design for specific tasks. Lastly, students will explore current research and propose future directions in Regularization and CNNs, contributing to advancements in Deep Learning methodologies.
2 readings
This module will lay the foundations that are needed to be successful in the field of Deep Learning. It will also introduce motivation for the field as well as discuss the history of the field.
3 videos1 reading2 assignments1 ungraded lab
This module will discuss the fundamentals of Machine Learning. You will explore different aspects of Machine Learning Algorithms and what is needed to create an algorithm.
1 video1 reading2 assignments1 ungraded lab
This module will discuss the building blocks of Deep Feedforward Neural Networks. Students will explore different parts of Deep Feedforward NN and what is needed to create and train the algorithms.
1 video1 reading2 assignments1 ungraded lab
This module will discuss the regularization in Deep Feedforward Neural Networks. Learners will explore the reasons for regularization along with different techniques.
1 video1 reading2 assignments1 ungraded lab
This module will discuss Convolutional Neural Networks. Students will explore the reasons for regularization along with different techniques.
1 video1 reading2 assignments1 ungraded lab
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