Deep Learning AZ Hands: Hands-On Artificial Neural Networks, is the name of a set of visual and data training in the field of data and analytics (Deep Learning). The training set ahead of you is a comprehensive course for learning deep learning algorithms in the field of artificial neural networks. By watching this course you will gain a very good understanding of artificial neural networks. You will also begin to learn the concepts and skills needed in this subject as you complete this tutorial from elementary to advanced.
You will need secondary school math information to view this course. You also need to be familiar with Python programming to get a better understanding of this video tutorial. Deep learning knowledge is very popular these days and with this knowledge you will be able to solve the complex problems you face.
Features of Deep Learning AZ دوره: Hands-On Artificial Neural Networks
Dear students, you will learn from the beginning the deep learning knowledge to the advanced level by observing and learning the concepts, techniques and skills presented in this video series. Also in this training series you will be fully acquainted with artificial intelligence. On the other hand, dear students, you will have over 180 video tutorials to complete the sessions of this course.
- Publisher: Udemy
- Instructor: Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team
- Level: Basic to Advanced
- Duration: 22:53:14
- Number of lessons: 189 lessons
- English language
Deep Learning AZ ™ Headlines: Hands-On Artificial Neural Networks
189 lectures 22:53:14
Welcome to the course
9 lectures 27:47
——————— Part 1 – Artificial Neural Networks ———————
1 lecture 00:15
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
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 lecture00: 38
7 lectures 01:11:42
Building an 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
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
10 lectures 39:13
Building an AutoEncoder
16 lectures 02:19:05
——————— 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
6 lectures 38:42
1 lecture 01:36
- High school mathematics level
- Basic Python programming knowledge
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This tutorial has English subtitles.
Video quality is 720p.
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Version 2019/4 compared to 2019/3 added a lesson and 3 minutes.
Version 2019/5 compared to 2019/4 Added two lessons and 1 minute total time.
Version 2019/7 increased to 1 lesson and 1 minute compared to 2019/5.
Version 2019/12 has not changed in the number of courses and times compared to 2019/7 and therefore has not been updated.
Version 2020 increased 1 lesson and 1 minute compared to 2019/12.
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