When will I have access to the lectures and assignments?

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

What will I get if I purchase the Certificate?

When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Fundamentals of Deep Learning

Instructor: Whizlabs Instructor

Included with

Learn more

2 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

6 hours to complete

Flexible schedule

Learn at your own pace

2 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

6 hours to complete

Flexible schedule

Learn at your own pace

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business

logos of Petrobras, TATA, Danone, Capgemini, P&G and L'Oreal

There are 2 modules in this course

Fundamentals of Deep Learning is a structured course designed for developers, data professionals, and AI enthusiasts who want to build a strong foundation in neural networks and modern deep learning techniques. This course focuses on core deep learning principles, including how artificial neurons work, forward and backward propagation, gradient descent optimization, activation functions, multi-class classification, Convolutional Neural Networks (CNNs), and transfer learning.

Through a progressive and practical learning path, you will gain hands-on experience training neural networks, evaluating model performance, and applying deep learning techniques to real-world image classification problems. The course bridges theory and implementation, helping you understand not just how models work, but why they work. Whether you are beginning your journey in artificial intelligence or preparing for advanced machine learning and cloud-based AI roles, this course equips you with the conceptual clarity and practical skills required to confidently build and evaluate deep learning models. This course includes approximately 3:30–4:00 hours of video lectures, combining foundational theory with step-by-step demonstrations. It is divided into focused modules that progressively develop your understanding of neural network architecture and applied deep learning techniques. To reinforce learning, each module includes quizzes and in-video practice questions that test conceptual understanding and practical application. 📘 Module 1: Foundations of Deep Learning and Neural Networks 🧠 Module 2: Deep Learning Models, Computer Vision, and Transfer Learning

Module details

Welcome to Week 1 of the Fundamentals of Deep Learning course. In this week, you will be introduced to the core concepts of deep learning and set clear expectations for what you will learn throughout the course. We will begin by understanding what deep learning is and how it fits within the broader fields of artificial intelligence and machine learning. You will explore how data is processed inside a neuron, gaining insight into the building blocks of neural networks. The week then focuses on how deep learning models learn, covering key concepts such as gradient descent, forward propagation, and backward propagation. Through demonstrations, you will see how a neuron is trained and how activation functions enable neural networks to learn complex, non-linear patterns. By the end of this week, you will have a strong foundational understanding of deep learning fundamentals, including how neural networks are structured, how learning and optimization take place, and the role of activation functions in training deep learning models.

What's included

9 videos2 readings2 assignments

9 videosTotal 53 minutes

What is Deep Learning?6 minutes
Expectations from Fundamentals of Deep Learning1 minute
How Data is Processed in a Neuron6 minutes
Gradient Descent9 minutes
Training a Neuron – Demo8 minutes
Deep Learning Neural Network – Forward Propagation4 minutes
Backward Propagation – Deep Learning Neural Network5 minutes
Activation Functions6 minutes
Activation Functions – Demo9 minutes

2 readingsTotal 60 minutes

Welcome to the Course30 minutes
Overview of Foundations of Deep Learning and Neural Networks30 minutes

2 assignmentsTotal 75 minutes

Core Concepts and Learning Mechanics of Deep Learning - Knowledge Check40 minutes
Foundations of Deep Learning and Neural Networks - Assessment35 minutes

Welcome to Week 2 of the Fundamentals of Deep Learning course. This week focuses on the practical application of deep learning techniques for real-world problems, with an emphasis on model training, evaluation, and modern neural network architectures. You will begin by working on multi-class classification using the MNIST dataset, where you will train and evaluate a deep learning model and understand how performance is measured. The week then introduces Convolutional Neural Networks (CNNs), explaining how they are designed to effectively learn from image data. You will also explore transfer learning techniques, learning how pre-trained models can be reused and adapted for new tasks. Through hands-on demonstrations, you will implement transfer learning on an image dataset and evaluate model performance. By the end of this week, you will be able to train and evaluate deep learning models for classification tasks, understand CNN-based architectures, and apply transfer learning to efficiently solve image-based deep learning problems.

What's included

5 videos2 readings2 assignments

5 videosTotal 43 minutes

Multi-Class Classification with MNIST Dataset – Deep Learning14 minutes
Training Multiclass Classifier – Fit and Evaluate7 minutes
Understanding Convolutional Neural Networks (CNNs)9 minutes
Transfer Learning Techniques6 minutes
Implementing Transfer Learning on an Image Dataset – Demo6 minutes

2 readingsTotal 40 minutes

Overview of Deep Learning Models, Computer Vision, and Transfer Learning30 minutes
What's Next ?10 minutes

2 assignmentsTotal 60 minutes

Applied Deep Learning: CNNs, Classification, and Transfer Learning - Knowledge Check30 minutes
Deep Learning Models, Computer Vision, and Transfer Learning - Assessment30 minutes