Deep Learning Advanced Computer Vision (GANs, SSD, +More!)

Deep Learning Advanced Computer Vision (GANs, SSD, +More!). Computer vision is a field of artificial intelligence that deals with the problem of understanding and analyzing images. Deep learning is a type of AI that has been particularly successful at recognizing patterns in large sets of data. This technology has been used to develop deep learning algorithms for computer vision tasks, such as object recognition, facial recognition, and scene understanding. Advanced computer vision techniques include deep learning GANs (generative adversarial networks), deep learning SSDs (single shot detection), and other advanced machine learning algorithms.

This course will cover advanced computer vision techniques, such as deep learning GANs (generative adversarial networks), deep learning SSDs (single shot detection), and other advanced machine learning algorithms.

This course will cover advanced computer vision techniques, such as deep learning GANs (generative adversarial networks), deep learning SSDs (single shot detection), and other advanced machine learning algorithms. Additionally, we will cover the basics of deep learning and how it can be used to improve image recognition.

  • Understand and apply transfer learning
  • Understand and use state-of-the-art convolutional neural nets such as VGG, ResNet, and Inception
  • Understand and use object detection algorithms like SSD
  • Understand and apply neural style transfer
  • Understand state-of-the-art computer vision topics
  • Class Activation Maps
  • GANs (Generative Adversarial Networks)
  • Object Localization Implementation Project

Deep Learning Advanced Computer Vision (GANs, SSD, +More!) Course Requirements

  • Know how to build, train, and use a CNN using some library (preferably in Python)
  • Understand basic theoretical concepts behind convolution and neural networks
  • Decent Python coding skills, preferably in data science and the Numpy Stack

Deep Learning: Advanced Computer Vision (GANs, SSD, +More!) Course Description

Most recent update: Instead of SSD, I tell you the best way to utilize RetinaNet, which is better and more present day. I tell you both the best way to utilize a pretrained model and how to prepare one yourself with a custom dataset on Google Colab.

This is perhaps the most energizing courses I’ve done and it truly shows how quick and how far profound learning has come throughout the long term.

At the point when I initially began my profound learning arrangement, I didn’t actually consider that I’d make two seminars on convolutional neural organizations.

I think what you’ll discover is that, this course is so totally not quite the same as the past one, you will be dazzled at exactly how much material we need to cover.

Allow me to give you a speedy overview of what is the issue here:

We will overcome any barrier between the fundamental CNN design you definitely know and love, to present day, novel structures, for example, VGG, ResNet, and Inception (named after the film which coincidentally, is likewise extraordinary!)

We will apply these to pictures of platelets, and make a framework that is a preferred clinical master over possibly you or I. This raises an intriguing thought: that the specialists of things to come are not people, but rather robots.

In this course, you’ll perceive how we can transform a CNN into an item discovery framework, that arranges pictures as well as can find each protest in a picture and anticipate its mark.

You can envision that such an undertaking is a fundamental essential for self-driving vehicles. (It should have the option to distinguish vehicles, walkers, bikes, traffic signals, and so on continuously)

We’ll be taking a gander at a best in class calculation called SSD which is both quicker and more precise than its archetypes.

Another exceptionally mainstream PC vision task that utilizes CNNs is called neural style move.

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This is the place where you take one picture called the substance picture, and another picture called the style picture, and you join these to make an altogether new picture, that is as though you employed a painter to paint the substance of the primary picture with the style of the other. In contrast to a human painter, this should be possible surprisingly fast.

I will likewise acquaint you with the now-well known GAN design (Generative Adversarial Networks), where you will become familiar with a portion of the innovation behind how neural organizations are utilized to create cutting edge, photograph practical pictures.

At present, we likewise execute object confinement, which is a fundamental initial move toward actualizing a full article discovery framework.

I trust you’re eager to find out about these high level utilizations of CNNs, I’ll see you in class!

Magnificent FACTS:

One of the significant topics of this course is that we’re moving endlessly from the CNN itself, to frameworks including CNNs.

Rather than zeroing in on the itemized internal operations of CNNs (which we’ve just done), we’ll center around significant level structure blocks. The outcome? Very nearly zero math.

Another outcome? No convoluted low-level code, for example, that written in Tensorflow, Theano, or PyTorch (albeit some discretionary activities may contain them for the exceptionally progressed understudies). The vast majority of the course will be in Keras which implies a ton of the monotonous, dull stuff is composed for you.

“In the event that you can’t execute it, you don’t get it”

Or on the other hand as the extraordinary physicist Richard Feynman stated: “What I can’t make, I don’t comprehend”.

My courses are the ONLY courses where you will figure out how to actualize AI calculations without any preparation

Different courses will show you how to connect your information into a library, yet do you truly require help with 3 lines of code?

Subsequent to doing likewise with 10 datasets, you understand you didn’t learn 10 things. You learned 1 thing, and just rehashed similar 3 lines of code multiple times…

Recommended Prerequisites:

Expertise to fabricate, train, and utilize a CNN utilizing some library (ideally in Python)

Comprehend fundamental hypothetical ideas driving convolution and neural organizations

Fair Python coding abilities, ideally in information science and the Numpy Stack


Look at the talk “AI and AI Prerequisite Roadmap” (accessible in the FAQ of any of my courses, including the free Numpy course)

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Who this course is for:

  • Understudies and experts who need to take their insight into PC vision and profound figuring out how to the following level
  • Any individual who needs to find out about item location calculations like SSD and YOLO
  • Any individual who needs to figure out how to compose code for neural style move
  • Any individual who needs to utilize move learning
  • Any individual who needs to abbreviate preparing time and fabricate the best in class PC vision nets quick


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