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

VGG, ResNet, Inception, SSD, RetinaNet, Neural Style Transfer, GANs +More in Tensorflow, Keras, and Python
4.7
4.7/5
(4,154 reviews)
25,829 students
Created by

9.9

CourseMarks Score®

10.0

Freshness

9.2

Feedback

9.8

Content

Platform: Udemy
Video: 14h 53m
Language: English
Next start: On Demand

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Detailed Analysis

CourseMarks Score®

9.9 / 10

CourseMarks Score® helps students to find the best classes. We aggregate 18 factors, including freshness, student feedback and content diversity.

Freshness Score

10.0 / 10
This course was last updated on 9/2021 .

Course content can become outdated quite quickly. After analysing 71,530 courses, we found that the highest rated courses are updated every year. If a course has not been updated for more than 2 years, you should carefully evaluate the course before enrolling.

Student Feedback

9.2 / 10
We analyzed factors such as the rating (4.7/5) and the ratio between the number of reviews and the number of students, which is a great signal of student commitment.

New courses are hard to evaluate because there are no or just a few student ratings, but Student Feedback Score helps you find great courses even with fewer reviews.

Content Score

9.8 / 10
Video Score: 9.9 / 10
The course includes 14h 53m video content. Courses with more videos usually have a higher average rating. We have found that the sweet spot is 16 hours of video, which is long enough to teach a topic comprehensively, but not overwhelming. Courses over 16 hours of video gets the maximum score.
The average video length is 8 hours 47 minutes of 111 Deep Learning courses on Udemy.
Detail Score: 10.0 / 10

The top online course contains a detailed description of the course, what you will learn and also a detailed description about the instructor.

Extra Content Score: 9.5 / 10

Tests, exercises, articles and other resources help students to better understand and deepen their understanding of the topic.

This course contains:

0 article.
0 resource.
0 exercise.
0 test.

Table of contents

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)

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

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

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

How much does the Deep Learning: Advanced Computer Vision (GANs, SSD, +More!) course cost? Is it worth it?

The course costs $19.99. And currently there is a 82% discount on the original price of the course, which was $109.99. So you save $90 if you enroll the course now.
The average price is $15.1 of 111 Deep Learning courses on Udemy.

Does the Deep Learning: Advanced Computer Vision (GANs, SSD, +More!) course have a money back guarantee or refund policy?

YES, Deep Learning: Advanced Computer Vision (GANs, SSD, +More!) has a 30-day money back guarantee. The 30-day refund policy is designed to allow students to study without risk.

Are there any SCHOLARSHIPS for this course?

Currently we could not find a scholarship for the Deep Learning: Advanced Computer Vision (GANs, SSD, +More!) course, but there is a $90 discount from the original price ($109.99). So the current price is just $19.99.

Who is the instructor? Is Lazy Programmer Inc. a SCAM or a TRUSTED instructor?

Lazy Programmer Inc. has created 29 courses that got 119,482 reviews which are generally positive. Lazy Programmer Inc. has taught 468,493 students and received a 4.6 average review out of 119,482 reviews. Depending on the information available, Lazy Programmer Inc. is a TRUSTED instructor.
Artificial intelligence and machine learning engineer
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.

9.9

CourseMarks Score®

10.0

Freshness

9.2

Feedback

9.8

Content

Platform: Udemy
Video: 14h 53m
Language: English
Next start: On Demand

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