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Introduction to Neural Networks

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Introduction to Neural Networks

This course is part of Foundations of Neural Networks Specialization

Instructor: Zerotti Woods

3,342 already enrolled

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5 modules

Gain insight into a topic and learn the fundamentals.

10 reviews

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

10 reviews

Intermediate level

Recommended experience

2 weeks to complete

at 10 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

Shareable certificate

Add to your LinkedIn profile

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.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 5 modules in this course

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.

Module details

This module will provide a comprehensive overview of the course and lay the foundations needed to be successful in the field of Deep Learning. It will also introduce motivation for the field and discuss the history of the field.

What's included

3 videos3 readings2 assignments1 ungraded lab

3 videosTotal 41 minutes

Introduction and Background16 minutes
History, Overview, and Foundations11 minutes
Foundational Mathematics for Deep Learning14 minutes

3 readingsTotal 40 minutes

Course Overview5 minutes
Instructor Biography: Prof. Zerotti Woods5 minutes
Reading References30 minutes

2 assignmentsTotal 75 minutes

History and Overview15 minutes
Overview and Foundations60 minutes

1 ungraded labTotal 60 minutes

Hands-On Lab: Closest and Furthest Points on a Circle60 minutes

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 video2 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.