Fundamentals of Deep Learning

Overview

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

Syllabus

• Foundations of Deep Learning and Neural Networks
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.
• Deep Learning Models, Computer Vision, and Transfer Learning
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.

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Taught by

Whizlabs Instructor

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Subjects

Computer Science