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:

One of the major themes of this course is that we’re moving away from the CNN itself, to systems involving CNNs.

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.

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”

Or as the great physicist Richard Feynman said: “What I cannot create, I do not understand”.

My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

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?

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:

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

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

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

Every line of code explained in detail – email me any time if you disagree

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

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: Beginners 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: Whats 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

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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