Practical Deep Learning for Computer Vision with Python

Practical Deep Learning for Computer Vision with Python

David Landup
Jovana Ninkovic


Early Access "Practical Deep Learning for Computer Vision with Python"

When you look down at your coffee mug, you're not at all surprised by the object in front of you. You've been able to do that since you were much younger than you are now. If you can read this - you've already trained yourself in visual pattern recognition, and context recognition, stringing together words in a sentence to form a coherent message. Way before you can read, even as a baby, you can distinguish between a cat and a dog, as well as between a bird and a whale. Whether you're aware of the labels we've assigned to them ("dog", "chien", "собака", "Hund", etc.) doesn't change the fact that the features that make up a dog are different from the features that make up a whale.

Vision is one of the dominant senses in humans.

How do we imbue computers with vision? These days, deep learning is by far the most promising candidate. Computer vision is a thriving, vast and thrilling field, with discoveries being made day in and day out. The entirety of the field isn't near being solved quite yet, but the results are already superhuman.

Real-Time OCR via Google Translate, translating from English to Japanese

Through your phone, you can perform real-time OCR via Google Translate, translating between languages on the fly.

MTheiler's Image from Wikipedia, highlighting object recognition capabilities of a YOLOv3 Network, Creative Commons

MTheiler's Image from Wikipedia, highlighting object recognition capabilities of a YOLOv3 Network, Creative Commons

Through object recognition systems, you can learn to identify, localize and recognize objects, taking steps and automating processes based on this knowledge.

TensorFlow's MoveNet Overview:

TensorFlow's MoveNet Overview

Through pose estimation - you can power the next generation of world-to-3D software, enabling robotics, filmography and personal expression through virtual and augmented reality.

Note: This is an early-access version of the course, which will be launched by the end of July 2022. By enrolling now, you'll get access to the Work-In-Progress version updated every few days, secure your seat for the full course at a fraction of the price, and get a DRM-free, downloadable PDF eBook, as soon as the course is finished.

Release and Pricing Timeline

Yet Another Computer Vision Course?

We won't be doing classification of MNIST digits or MNIST fashion. They served their part a long time ago. Too many learning resources are focusing on basic datasets and basic architectures before letting advanced black-box architectures shoulder the burden of performance.

We want to focus on demystification, practicality, understanding, intuition and real projects. Want to learn how you can make a difference? We'll take you on a ride from the way our brains process images to writing a research-grade deep learning classifier for breast cancer to deep learning networks that "hallucinate", teaching you the principles and theory through practical work, equipping you with the know-how and tools to become an expert at applying deep learning to solve computer vision.

What's Inside?

  • The first principles of vision and how computers can be taught to "see"
  • Different tasks and applications of computer vision
  • The tools of the trade that will make your work easier
  • Finding, creating and utilizing datasets for computer vision
  • The theory and application of Convolutional Neural Networks
  • Transfer Learning and utilizing others' training time and computational resources for your benefit
  • How to apply a healthy dose of skepticism to mainstream ideas and understand the implications of widely adopted techniques
  • Case studies of how companies use computer vision techniques to achieve better results
  • Proper model evaluation, latent space visualization and identifying the model's attention
  • Performing domain research, processing your own datasets and establishing model tests
  • Cutting-edge architectures, the progression of ideas, what makes them unique and how to implement them
  • Selecting models depending on your application
  • Creating an end-to-end machine learning pipeline
  • Quickly and easily creating custom labeled computer vision datasets
  • Creating a reverse image search engine
  • Image segmentation
  • Object and Face Recognition
  • Image captioning with CNNs and Transformers
  • DeepDream

How the Course is Structured

The course is structured through Guides and Guided Projects.

Guides serve as an introduction to a topic, such as the following introduction and guide to Convolutional Neural Networks, and assume no prior knowledge in the narrow field.

Guided Projects are self-contained and serve to bridge the gap between the cleanly formatted theory and practice and put you knee-deep into the burning problems and questions in the field. With Guided Projects, we presume only the knowledge of the narrower field that you could gain from following the lessons in the course. You can also enroll into Guided Projects as individual mini-courses, though, you gain access to all relevant Guided Projects by enrolling into this course.

Once we've finished reviewing how they're built, we'll assess why we'd want to build them. Theory is theory and practice is practice. Any theory will necessarily be a bit behind the curve - it takes time to produce resources like books and courses, and it's not easy to "just update them".

Guided Projects are our attempt at making our courses stay relevant through the years of research and advancement. Theory doesn't change as fast. The application of that theory does.

In the first lessons, we'll jump into Convolutional Neural Networks - how they work, what they're made of and how to build them, followed by an overview of some of the modern architectures. This is quickly followed by a real project with imperfect data, a lesson on critical thinking, important techniques and further projects.


Downloadable Resources

Lesson 2 Notebook(942 KB)
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Lesson 3 Notebook(21 MB)
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Lesson 4 Notebook(1 MB)
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Lesson 5 Notebook(2 MB)
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Lesson 6 Notebook(3 MB)
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Last Updated: Jun 2022

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