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Computer Vision Masterclass: Build Advanced CNNs
Rating: 3.2 out of 5(33 ratings)
202 students

Computer Vision Masterclass: Build Advanced CNNs

Build real-world Image Classification, Object Detection, and Style Transfer apps. Master Convolutional Neural Networks.
Created bySkillbox, LLC
Last updated 1/2026
English

What you'll learn

  • Get a practical deep dive into machine learning and deep learning algorithms
  • Explore CNN applications, visualization, and image enhancement
  • Understand the advantages and trade-offs of various CNN architectures
  • Understand how convolution can be applied to image effects
  • Understand how convolution helps image classification

Course content

6 sections • 17 lectures • 1h 50m total length

  • Introduction1:40
  • The Power of Convolutional Neural Networks in Image Recognition2:16

Requirements

  • Intermediate Python programming skills (Functions, Loops, NumPy).
  • Basic understanding of Neural Networks (Weights, Biases).
  • A computer with a dedicated GPU is recommended

Description

Machines can now "see" better than humans. It’s time you learned how.

Computer Vision is the technology behind self-driving cars, facial recognition, and medical diagnostics. At the heart of this revolution is the Convolutional Neural Network (CNN).

Welcome to Deep Learning: CNNs for Visual Recognition. This course is not just a theoretical overview; it is a hands-on guide to building the intelligent systems that perceive the world. Whether you are a Data Scientist looking to specialize or a Developer wanting to build AI-powered apps, this course bridges the gap between research papers and working code.

Why this course? Many courses drown you in math without showing you the code. We take a "Code-First" approach. You will understand the architecture of layers, filters, and pooling by building them, seeing the results, and tuning them for high performance.

What will you build? You will move beyond basic digit recognition (MNIST) and tackle advanced visual tasks:

  • Image Classification: Build models that can distinguish between complex objects in photos.

  • Object Detection: Learn how machines draw bounding boxes around objects in real-time.

  • Neural Style Transfer: Recreate the artistic style of Van Gogh or Picasso on your own photos using Deep Learning.

  • Generative Adversarial Networks (GANs): Pit two neural networks against each other to generate entirely new, realistic images from scratch.

  • Super-Resolution: Use AI to upscale low-quality images into high-definition masterpieces.

What’s Inside the Curriculum?

  1. The Anatomy of a CNN: Master Kernels, Stride, Padding, and Pooling layers.

  2. Modern Architectures: Understand the evolution of state-of-the-art models (VGG, ResNet, Inception) and why they work.

  3. Visualizing AI: Open the "Black Box" and see exactly what the neural network sees at every layer.

  4. DeepDream & Art: Explore the psychedelic side of AI by amplifying patterns in images.

Prerequisites:

  • Basic understanding of Python programming.

  • Familiarity with the basics of Machine Learning (Regression/Classification).

  • No prior experience with Computer Vision is required—we start from the pixels up.

Don't just watch the AI revolution happen—build it. Enroll today and start mastering the most powerful algorithms in Deep Learning.

Who this course is for:

  • Software engineers
  • Students and professional computer scientists
  • Anyone who wants to apply deep learning to images

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