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.
Introduction to Neural Networks
Introduction to Neural Networks
This course is part of Foundations of Neural Networks Specialization
Instructor: Zerotti Woods
Included with 
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
19 hours to complete
3 weeks at 6 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
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.
Skills you'll gain
Details to know
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Recently updated!
December 2024
Assessments
10 assignments
Taught in English
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This course is part of the Foundations of Neural Networks Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 6 modules in this course
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.
What's included
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.
What's included
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.
What's included
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.
What's included
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.
What's included
1 video1 reading2 assignments1 ungraded lab
This module will discuss Convolutional Neural Networks. Students will explore the reasons for regularization along with different techniques.
What's included
1 video1 reading2 assignments1 ungraded lab
Instructor
Offered by
Recommended if you're interested in Algorithms
Johns Hopkins University
Johns Hopkins University
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