What computer vision topics are covered in the Deep Learning Computer Vision Masterclass?

The course covers image classification, CNNs, backpropagation, optimization, attention, object detection, segmentation, 3D vision, video analysis, and generative models.

Why are GPUs faster than CPUs for training computer vision deep learning models?

GPUs can perform many matrix and tensor operations in parallel, making them highly efficient for the large-scale computations used in neural networks.

What is ROI Align used for in object detection and segmentation?

ROI Align precisely preserves spatial alignment when extracting region features, avoiding the coordinate rounding errors introduced by ROI Pool.

Free Course Image Deep Learning Computer Vision Masterclass

Free online course Deep Learning Computer Vision Masterclass

Duration of the online course: 48 hours and 19 minutes

3.8

(4)

Build real computer vision skills with a free deep learning course—CNNs, detection, segmentation, and training tips to boost your AI portfolio fast.

In this free course, learn about

Deep Learning Foundations
Convolutional Networks and Computing Infrastructure
Training Deep Neural Networks
Sequential Models and Attention
Computer Vision Applications
Generative Models, Reinforcement Learning, and Course Conclusion

About the free online course

Developing reliable computer vision systems is no longer reserved for large research labs. With today’s tools and hardware, you can train models that recognize images, locate objects, segment scenes, and even learn meaningful representations from data—if you understand the foundations that make deep learning work. This course guides you from core principles to modern practices, connecting the why behind the math with the how of real-world model building.

You’ll start by framing the key ideas in image recognition, then build intuition for classifiers, optimization, and the training loop that powers neural networks. Instead of treating models like black boxes, you’ll learn how choices such as validation strategy, hyperparameters, activations, and learning rate schedules impact performance and stability. Along the way, you’ll strengthen your grasp of backpropagation and computational graphs so you can reason about gradients, debug training issues, and make improvements confidently.

As you progress, convolutional networks take center stage. You’ll explore why convolutions and nonlinearities are essential, how widely used CNN architectures evolved, and what residual connections accomplish when models get deep. Practical awareness of compute also matters: understanding why GPUs accelerate matrix operations helps you make better decisions about tooling, efficiency, and scaling experiments.

The course goes beyond classification to the broader computer vision toolkit. You’ll develop the mental model behind object detection outputs, the transition from detection to segmentation, and the alignment ideas that improved region-based methods. You’ll also gain perspective on 3D vision representations and how video data is commonly fed into deep networks, preparing you to work with temporal and spatial complexity.

To round out your skill set, you’ll touch on attention and sequence modeling concepts, learn how modern methods interpret and visualize what networks attend to, and build a clearer distinction between discriminative and generative modeling. You’ll see how objectives such as the KL term in VAEs shape learned representations, and you’ll get a practical overview of reinforcement learning goals and framing. By the end, you’ll have a cohesive understanding of the ideas that fueled the computer vision deep learning boom and the confidence to apply them to projects, portfolios, and advanced study.

Course content

Video class: Lecture 1: Introduction to Deep Learning for Computer Vision 57m
Exercise: What combination of factors most directly helped drive the major deep learning breakthrough in computer vision around 2012?
Video class: Lecture 2: Image Classification 1h02m
Exercise: Why is it important to use a separate validation set (and not the test set) when choosing hyperparameters like K in k-nearest neighbors?
Video class: Lecture 3: Linear Classifiers 1h02m
Exercise: In a linear classifier for CIFAR-10, what is the shape of the weight matrix W when an image is flattened into a 3072-dimensional vector and there are 10 classes?
Video class: Lecture 4: Optimization 1h03m
Exercise: Why is stochastic gradient descent (SGD) typically preferred over full-batch gradient descent when training deep learning models on large datasets?
Video class: Lecture 5: Neural Networks 1h02m
Exercise: Why is including a non-linear activation function (like ReLU) between two weight matrices critical in a neural network?
Video class: Lecture 6: Backpropagation 1h11m
Exercise: What is the main purpose of using a computational graph with backpropagation in deep learning?

Video class: Lecture 7: Convolutional Networks 1h08m
Exercise: Why are nonlinear activation functions (e.g., ReLU) typically inserted between convolution layers in a CNN?
Video class: Lecture 8: CNN Architectures 1h12m
Exercise: What is the main purpose of adding a residual (skip) connection in a residual block?
Video class: Lecture 9: Hardware and Software 1h12m
Exercise: What is one key reason GPUs are often much faster than CPUs for deep learning operations like matrix multiplication?

Video class: Lecture 10: Training Neural Networks I 1h12m
Exercise: Why is the sigmoid activation function often avoided when training deep neural networks?
Video class: Lecture 11: Training Neural Networks II 1h19m
Exercise: Why is a cosine learning rate decay schedule often considered easier to tune than a step schedule?

Video class: Lecture 12: Recurrent Networks 1h13m
Exercise: Why do recurrent neural networks (RNNs) use the same weights at every time step?
Video class: Lecture 13: Attention 1h11m
Exercise: In an attention-based sequence-to-sequence model, how is the context vector for a decoder time step typically computed?

Video class: Lecture 14: Visualizing and Understanding 1h12m
Exercise: When performing nearest-neighbor image retrieval using a CNN, which representation is used to compare images in the described approach?
Video class: Lecture 15: Object Detection 1h12m
Exercise: In object detection, what does the model typically output for each detected object?
Video class: Lecture 16: Detection and Segmentation 1h10m
Exercise: What key improvement does ROI Align introduce compared to ROI Pool in region-based detectors?
Video class: Lecture 17: 3D Vision 1h12m
Exercise: Which 3D shape representation assigns a distance value to each pixel, but cannot represent occluded parts of the scene?
Video class: Lecture 18: Videos 1h15m
Exercise: In video classification, how is a video most commonly represented as input to a deep network?

Video class: Lecture 19: Generative Models I 1h11m
Exercise: What is the key probabilistic difference between a discriminative model and a generative model?
Video class: Lecture 20: Generative Models II 1h12m
Exercise: In a variational autoencoder (VAE), what role does the KL divergence term play in the training objective?
Video class: Lecture 21: Reinforcement Learning 1h11m
Exercise: In reinforcement learning, what is the main objective of the agent?
Video class: Lecture 22: Conclusion 1h13m
Exercise: What combination was highlighted as a major driver of the deep learning boom in computer vision around the ImageNet era?

Learn aboutDeep Learning

Explore the best free online Deep Learning courses and build practical skills in neural networks, CNNs, RNNs, transformers, computer vision, and AI. Learn at your own pace with expert-led lessons, hands-on projects, and real-world examples. Every course is free and includes a certificate to help you advance your career in machine learning and artificial intelligence.

Learn aboutComputer Vision

Explore free online Computer Vision courses with certificates and build practical skills in image processing, object detection, facial recognition, deep learning, OpenCV, and neural networks. Learn at your own pace from beginner to advanced levels, strengthen your AI expertise, and earn a certificate to showcase your career-ready knowledge.