Beyond MNIST Example: Practical Convolutional NNs

Name: Beyond MNIST Example: Practical Convolutional NNs
Brand: Udemy
SKU: 1909704
Rating: 3.9995 (9 reviews)

If you are tired of not going further step after watching MNIST Tutorial, you are welcome!

Ratings 4.00 / 5.00

Beyond MNIST Example: Practical Convolutional NNs

What You Will Learn!

Enrolled students can easily code all the subprocesses of convolutional neural network.
Enrolled students can distinguish the problem of not-working Convolutional Neural Networks.
Enrolled students can apply several solutions for the loading images on Python.
Enrolled students can understand and apply transfer learning.
Enrolled students can diagnose imbalanced dataset problem.
Enrolled students can solve imbalanced dataset problem with different methods.
Enrolled students can apply several solutions for the image augmentation.
Enrolled students can create their CustomDataGenerator Functions for Keras.

Description

From the owner of Makine Öğrenmesi (Machine Learning) channel on YouTube that has 4500+ subscribers that is nearly active for 2 years.

makineogrenmesi

Hi. I am Burak, Industrial Engineering student from Bilkent University. I'm working on Machine Learning/Deep Learning for two years and I have an experience on education/practise of Convolutional Neural Network.

Nearly 700,000 minutes of Watch Time. Nearly 170,000 view. Have been 700+ conversion on their problems with subscribers.

I know problems you can face with. My plan is to make you familiar with problems under my control to learn better.

You probably started to learn convolutional neural network with MNIST Tutorial, which is a good example.

However, there are some untold mysteries about it. When you tried to apply CNN to your dataset, you probably had problems that you do not know. What are these problems that can be seen when we take the lid off(easy to hard):

Image Size
Data Size
Arranging the Data
Loading Images
Preparing Data (with several techniques)
Imbalanced Dataset
Several solutions to Imbalanced Dataset
Small Data
Solutions to Small Data
Image Augmentation (Easily handling it with Keras)
Extreme Image Augmentation using dedicated libraries that can be implemented easily on Keras' fit functions.

This is not a course that I only talk about concepts briefly and code it once. All methods are the methods that you can use for general CNN problems.

Also, we will be in direct contact in Udemy Platform. Every advises and problems will be considered by me. So you aren't only registering for course, you can contact with me for any case of deep learning. I will try to help you about the concepts/codes that we are interested in the course.

Who Should Attend!

Deep Learning Enthusiasts that has trouble going one more step after MNIST example and programmers who need practice on using Python libraries that are directly/indirectly related to Deep Learning Libraries such as Tensorflow, PyTorch, Keras
Python beginners who works on Python for Deep Learning and Machine Learning
If you need harsh image augmentation, you should be here.

TAKE THIS COURSE