Description
Deep Learning A-Z: Hands-On Artificial Neural Networks is the name of the Data & Analytics video training suite and Deep Learning. The training set for you is a comprehensive training course for learning deep learning algorithms in the field of artificial neural networks. By observing this course, you will have a very good understanding of artificial neural networks. You will also begin to learn the concepts and skills you need in this tutorial by looking at this complete tutorial from elementary to advanced level.
You will need to have high school math information to view this course. Also, to better understand the tutorials of this video course, you need to be familiar with Python programming. Deep learning knowledge has many fans these days and with the help of this knowledge you will be able to solve complex problems ahead of you.
Features of Deep Learning A-Z: Hands-On Artificial Neural Networks
Dear students, you will learn from the beginning with deep learning knowledge to advanced level by observing and learning the concepts, techniques and skills presented in this visual collection. You will also be fully acquainted with Artificial Intelligence in this training suite. On the other hand, your distinguished students will have more than 180 training sessions to complete the training sessions.
Course specifications:
- Publisher: Udemy
- Teacher: Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team
- Level: Advanced to advanced
- Duration: 22:47:42
- Number of lessons: 184 lessons
- English language
- Deep Learning A-Z Headings: Hands-On Artificial Neural Networks
- Course content: 184 lectures 22:47:42
Contents
Welcome to the course: 5 lectures 24:44
------- Part 1 - Artificial Neural Networks ------
1 lecture 00:15
ANN Intuition: 8 lectures 01:17:37
Building an ANN: 14 lectures 01:22:31
Homework Challenge - Should we say goodbye to that customer?: 2 lectures 13:24
Evaluating, Improving and Tuning the ANN: 3 lectures 46:39
Homework Challenge - Put me one step down on the podium: 1 lecture 00:13
------- Part 2 - Convolutional Neural Networks ------
1 lecture 00:09
CNN Intuition: 10 lectures 01:40:55
Building a CNN: 13 lectures 01:19:21
Homework - What's that pet?: 2 lectures 16:40
Evaluating, Improving and Tuning the CNN: 2 lectures 00:32
-------- Part 3 - Recurrent Neural Networks --------
1 lecture 00:38
RNN Intuition: 7 lectures 01:11:42
Building a RNN: 17 lectures 02:01:42
Evaluating, Improving and Tuning the RNN: 3 lectures 02:24
-------- Part 4 - Self Organizing Maps --------
1 lecture 00:10
SOMs Intuition: 10 lectures 01:30:50
Building a SOM: 5 lectures 53:29
Mega Case Study: 4 lectures 30:44
--------- Part 5 - Boltzmann Machines ---------
1 lecture 00:47
Boltzmann Machine Intuition: 8 lectures 01:13:10
Building a Boltzmann Machine: 19 lectures 02:51:16
---------- Part 6 - AutoEncoders ----------
1 lecture 00:32
AutoEncoders Intuition: 10 lectures 39:13
Building an AutoEncoder: 15 lectures 02:16:25
------- Annex - Get the Machine Learning Basics ------
1 lecture 00:29
Regression & Classification Intuition: 5 lectures 27:14
Data Preprocessing Template: 7 lectures 01:04:04
Classification Template: 6 lectures 38:42
Bonus lectures: 1 lecture01: 53
prerequisites:
- High school mathematics level
- Basic Python programming knowledge
