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Development Data Science Deep Learning

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

VGG, ResNet, Inception, SSD, RetinaNet, Neural Style Transfer, GANs +More in Tensorflow, Keras, and Python
Rating: 4.7 out of 54.7 (3,621 ratings)
23,102 students
Created by Lazy Programmer Inc.
Last updated 1/2021
English
English [Auto], Italian [Auto], 
30-Day Money-Back Guarantee

What you'll 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
Curated for the Udemy for Business collection

Course content

16 sections • 119 lectures • 15h 9m total length

  • Preview02:35
  • Preview06:49
  • Where to get the code
    08:26
  • Anyone Can Succeed in this Course
    12:42

  • What is Machine Learning?
    14:26
  • Code Preparation (Classification Theory)
    15:59
  • Beginner's Code Preamble
    04:38
  • Classification Notebook
    08:40
  • Code Preparation (Regression Theory)
    07:18
  • Regression Notebook
    10:34
  • The Neuron
    09:58
  • How does a model "learn"?
    10:53
  • Making Predictions
    06:45
  • Saving and Loading a Model
    04:27
  • Suggestion Box
    03:03

  • Artificial Neural Networks Section Introduction
    06:00
  • Forward Propagation
    09:40
  • The Geometrical Picture
    09:43
  • Activation Functions
    17:18
  • Multiclass Classification
    08:41
  • How to Represent Images
    12:36
  • Code Preparation (ANN)
    12:42
  • ANN for Image Classification
    08:36
  • ANN for Regression
    11:05

  • What is Convolution? (part 1)
    16:38
  • What is Convolution? (part 2)
    05:56
  • What is Convolution? (part 3)
    06:41
  • Convolution on Color Images
    15:58
  • CNN Architecture
    20:58
  • CNN Code Preparation
    15:13
  • CNN for Fashion MNIST
    06:46
  • CNN for CIFAR-10
    04:28
  • Data Augmentation
    08:51
  • Batch Normalization
    05:14
  • Improving CIFAR-10 Results
    10:22

  • VGG Section Intro
    03:04
  • What's so special about VGG?
    07:00
  • Transfer Learning
    08:22
  • Relationship to Greedy Layer-Wise Pretraining
    02:19
  • Getting the data
    02:17
  • Code pt 1
    09:23
  • Code pt 2
    03:41
  • Code pt 3
    03:27
  • VGG Section Summary
    01:47

  • ResNet Section Intro
    02:49
  • ResNet Architecture
    12:45
  • Building ResNet - Strategy
    02:25
  • Uh-oh! What Happens if the Implementation Changes?
    05:16
  • Building ResNet - Conv Block Details
    03:34
  • Building ResNet - Conv Block Code
    06:08
  • Building ResNet - Identity Block Details
    01:23
  • Building ResNet - First Few Layers
    02:27
  • Building ResNet - First Few Layers (Code)
    04:15
  • Building ResNet - Putting it all together
    04:19
  • Exercise: Apply ResNet
    01:16
  • Applying ResNet
    02:39
  • 1x1 Convolutions
    04:03
  • Optional: Inception
    06:47
  • Different sized images using the same network
    04:12
  • ResNet Section Summary
    02:27

  • Preview05:04
  • Object Localization
    06:36
  • What is Object Detection?
    02:53
  • How would you find an object in an image?
    08:40
  • The Problem of Scale
    03:47
  • The Problem of Shape
    03:52
  • 2020 Update - More Fun and Excitement
    05:45
  • Using Pretrained RetinaNet
    11:14
  • RetinaNet with Custom Dataset (pt 1)
    04:26
  • RetinaNet with Custom Dataset (pt 2)
    09:20
  • RetinaNet with Custom Dataset (pt 3)
    07:05
  • Optional: Intersection over Union & Non-max Suppression
    05:06
  • SSD Section Summary
    02:52

  • Preview02:52
  • Style Transfer Theory
    11:23
  • Optimizing the Loss
    08:02
  • Code pt 1
    07:46
  • Code pt 2
    07:13
  • Code pt 3
    03:50
  • Style Transfer Section Summary
    02:21

  • Class Activation Maps (Theory)
    07:09
  • Class Activation Maps (Code)
    09:54

  • GAN Theory
    15:51
  • GAN Colab Notebook
    00:00
  • GAN Code
    12:10

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

Description

Latest update: Instead of SSD, I show you how to use RetinaNet, which is better and more modern. I show you both how to use a pretrained model and how to train one yourself with a custom dataset on Google Colab.

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 SSD which 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)

Who this course is for:

  • 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

Instructor

Lazy Programmer Inc.
Artificial intelligence and machine learning engineer
Lazy Programmer Inc.
  • 4.6 Instructor Rating
  • 108,419 Reviews
  • 423,064 Students
  • 28 Courses

Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.

I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition.

Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.

I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.

My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.

I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. 

Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.

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