Deep Learning: Advanced Computer Vision (GANs, SSD, +More!)
Deep Learning: Advanced Computer Vision (GANs, SSD, +More!), available at $119.99, has an average rating of 4.71, with 144 lectures, based on 6366 reviews, and has 41074 subscribers.
You will learn about 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 Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion This course is ideal for individuals who are Students and professionals who want to take their knowledge of computer vision and deep learning to the next level or Anyone who wants to learn about object detection algorithms like SSD and YOLO or Anyone who wants to learn how to write code for neural style transfer or Anyone who wants to use transfer learning or Anyone who wants to shorten training time and build state-of-the-art computer vision nets fast It is particularly useful for Students and professionals who want to take their knowledge of computer vision and deep learning to the next level or Anyone who wants to learn about object detection algorithms like SSD and YOLO or Anyone who wants to learn how to write code for neural style transfer or Anyone who wants to use transfer learning or Anyone who wants to shorten training time and build state-of-the-art computer vision nets fast.
Enroll now: Deep Learning: Advanced Computer Vision (GANs, SSD, +More!)
Summary
Title: Deep Learning: Advanced Computer Vision (GANs, SSD, +More!)
Price: $119.99
Average Rating: 4.71
Number of Lectures: 144
Number of Published Lectures: 116
Number of Curriculum Items: 144
Number of Published Curriculum Objects: 116
Original Price: $199.99
Quality Status: approved
Status: Live
What You Will Learn
- 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
- Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion
Who Should Attend
- Students and professionals who want to take their knowledge of computer vision and deep learning to the next level
- Anyone who wants to learn about object detection algorithms like SSD and YOLO
- Anyone who wants to learn how to write code for neural style transfer
- Anyone who wants to use transfer learning
- Anyone who wants to shorten training time and build state-of-the-art computer vision nets fast
Target Audiences
- Students and professionals who want to take their knowledge of computer vision and deep learning to the next level
- Anyone who wants to learn about object detection algorithms like SSD and YOLO
- Anyone who wants to learn how to write code for neural style transfer
- Anyone who wants to use transfer learning
- Anyone who wants to shorten training time and build state-of-the-art computer vision nets fast
Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.
This is one of the most exciting courses I’ve done and it really shows how fast and how far deep learning has come over the years.
When I first started my deep learning series, I didn’t ever consider that I’d make two courses on convolutional neural networks.
I think what you’ll find is that, this course is so entirely different from the previous one, you will be impressed at just how much material we have to cover.
Let me give you a quick rundown of what this course is all about:
We’re going to bridge the gap between the basic CNN architecture you already know and love, to modern, novel architectures such as VGG, ResNet, and Inception (named after the movie which by the way, is also great!)
We’re going to apply these to images of blood cells, and create a system that is a better medical expert than either you or I. This brings up a fascinating idea: that the doctors of the future are not humans, but robots.
In this course, you’ll see how we can turn a CNN into an object detection system, that not only classifies images but can locate each object in an image and predict its label.
You can imagine that such a task is a basic prerequisite for self-driving vehicles. (It must be able to detect cars, pedestrians, bicycles, traffic lights, etc. in real-time)
We’ll be looking at a state-of-the-art algorithm called SSDwhich is both faster and more accurate than its predecessors.
Another very popular computer vision task that makes use of CNNs is called neural style transfer.
This is where you take one image called the content image, and another image called the style image, and you combine these to make an entirely new image, that is as if you hired a painter to paint the content of the first image with the style of the other. Unlike a human painter, this can be done in a matter of seconds.
I will also introduce you to the now-famous GAN architecture (Generative Adversarial Networks), where you will learn some of the technology behind how neural networks are used to generate state-of-the-art, photo-realistic images.
Currently, we also implement object localization, which is an essential first step toward implementing a full object detection system.
I hope you’re excited to learn about these advanced applications of CNNs, I’ll see you in class!
AWESOME FACTS:
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One of the major themes of this course is that we’re moving away from the CNN itself, to systems involving CNNs.
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Instead of focusing on the detailed inner workings of CNNs (which we’ve already done), we’ll focus on high-level building blocks. The result? Almost zero math.
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Another result? No complicated low-level code such as that written in Tensorflow, Theano, or PyTorch (although some optional exercises may contain them for the very advanced students). Most of the course will be in Keras which means a lot of the tedious, repetitive stuff is written for you.
“If you can’t implement it, you don’t understand it”
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Or as the great physicist Richard Feynman said: “What I cannot create, I do not understand”.
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My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch
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Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?
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After doing the same thing with 10 datasets, you realize you didn’t learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times…
Suggested Prerequisites:
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Know how to build, train, and use a CNN using some library (preferably in Python)
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Understand basic theoretical concepts behind convolution and neural networks
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Decent Python coding skills, preferably in data science and the Numpy Stack
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:
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Check out the lecture “Machine Learning and AI Prerequisite Roadmap” (available in the FAQ of any of my courses, including the free Numpy course)
UNIQUE FEATURES
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Every line of code explained in detail – email me any time if you disagree
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No wasted time “typing” on the keyboard like other courses – let’s be honest, nobody can really write code worth learning about in just 20 minutes from scratch
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Not afraid of university-level math – get important details about algorithms that other courses leave out
Course Curriculum
Chapter 1: Welcome
Lecture 1: Introduction
Lecture 2: Outline and Perspective
Lecture 3: How to Succeed in this Course
Chapter 2: Google Colab & Getting Setup
Lecture 1: Where to get the code, notebooks, and data
Lecture 2: Intro to Google Colab, how to use a GPU or TPU for free
Lecture 3: Uploading your own data to Google Colab
Lecture 4: Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn?
Lecture 5: Temporary 403 Errors
Chapter 3: Machine Learning Basics Review
Lecture 1: What is Machine Learning?
Lecture 2: Code Preparation (Classification Theory)
Lecture 3: Beginner's Code Preamble
Lecture 4: Classification Notebook
Lecture 5: Code Preparation (Regression Theory)
Lecture 6: Regression Notebook
Lecture 7: The Neuron
Lecture 8: How does a model "learn"?
Lecture 9: Making Predictions
Lecture 10: Saving and Loading a Model
Lecture 11: Suggestion Box
Chapter 4: Artificial Neural Networks (ANN) Review
Lecture 1: Artificial Neural Networks Section Introduction
Lecture 2: Forward Propagation
Lecture 3: The Geometrical Picture
Lecture 4: Activation Functions
Lecture 5: Multiclass Classification
Lecture 6: How to Represent Images
Lecture 7: Color Mixing Clarification
Lecture 8: Code Preparation (ANN)
Lecture 9: ANN for Image Classification
Lecture 10: ANN for Regression
Chapter 5: Convolutional Neural Networks (CNN) Review
Lecture 1: What is Convolution? (part 1)
Lecture 2: What is Convolution? (part 2)
Lecture 3: What is Convolution? (part 3)
Lecture 4: Convolution on Color Images
Lecture 5: CNN Architecture
Lecture 6: CNN Code Preparation
Lecture 7: CNN for Fashion MNIST
Lecture 8: CNN for CIFAR-10
Lecture 9: Data Augmentation
Lecture 10: Batch Normalization
Lecture 11: Improving CIFAR-10 Results
Chapter 6: VGG and Transfer Learning
Lecture 1: VGG Section Intro
Lecture 2: What's so special about VGG?
Lecture 3: Transfer Learning
Lecture 4: Relationship to Greedy Layer-Wise Pretraining
Lecture 5: Getting the data
Lecture 6: Code pt 1
Lecture 7: Code pt 2
Lecture 8: Code pt 3
Lecture 9: VGG Section Summary
Chapter 7: ResNet (and Inception)
Lecture 1: ResNet Section Intro
Lecture 2: ResNet Architecture
Lecture 3: Transfer Learning with ResNet in Code
Lecture 4: Blood Cell Images Dataset
Lecture 5: How to Build ResNet in Code
Lecture 6: 1×1 Convolutions
Lecture 7: Optional: Inception
Lecture 8: Different sized images using the same network
Lecture 9: ResNet Section Summary
Chapter 8: Object Detection (SSD / RetinaNet)
Lecture 1: SSD Section Intro
Lecture 2: Object Localization
Lecture 3: What is Object Detection?
Lecture 4: How would you find an object in an image?
Lecture 5: The Problem of Scale
Lecture 6: The Problem of Shape
Lecture 7: SSD Tensorflow Object Detection API (pt 1)
Lecture 8: SSD Tensorflow Object Detection API (pt 2)
Lecture 9: SSD for Video Object Detection
Lecture 10: Optional: Intersection over Union & Non-max Suppression
Lecture 11: SSD Section Summary
Chapter 9: Neural Style Transfer
Lecture 1: Style Transfer Section Intro
Lecture 2: Style Transfer Theory
Lecture 3: Optimizing the Loss
Lecture 4: Code pt 1
Lecture 5: Code pt 2
Lecture 6: Code pt 3
Lecture 7: Style Transfer Section Summary
Chapter 10: Class Activation Maps
Lecture 1: Class Activation Maps (Theory)
Lecture 2: Class Activation Maps (Code)
Chapter 11: GANs (Generative Adversarial Networks)
Lecture 1: GAN Theory
Lecture 2: GAN Code
Chapter 12: Object Localization Project
Lecture 1: Localization Introduction and Outline
Lecture 2: Localization Code Outline (pt 1)
Lecture 3: Localization Code (pt 1)
Lecture 4: Localization Code Outline (pt 2)
Lecture 5: Localization Code (pt 2)
Lecture 6: Localization Code Outline (pt 3)
Lecture 7: Localization Code (pt 3)
Lecture 8: Localization Code Outline (pt 4)
Instructors
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Lazy Programmer Inc.
Artificial intelligence and machine learning engineer
Rating Distribution
- 1 stars: 45 votes
- 2 stars: 56 votes
- 3 stars: 279 votes
- 4 stars: 1954 votes
- 5 stars: 4032 votes
Frequently Asked Questions
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