tuatini.me

GENERAL
META
HTML
LINK ANALYSIS
SERVER

Tuatini.me recives any estimated n/a unique visitors and n/a unique page views per day. Revenue gained from these much visits may be n/a per day from various advertising sources. The estimated worth of site is n/a.

Website Age n/a
Alexa Rank no-data
Country France
IP Address 163.172.136.150

META INFORMATION

Title

Tuatini's blog

Description

Thoughts, stories and ideas on Deep learning and Artificial intelligence

Keywords

Content Type

utf-8

No	Meta Name	Value
1	viewport	width=device-width, initial-scale=1

google ads

GENERAL HTML INFORMATION

Type	Status
HTML 5
Responsive Website

HTML SIZE INFORMATION

Text / Code Ratio 12.23 %

tuatini.me has a website text/code ratio of 12.23 %. Search engine crawlers tend to not pick up pages with inadequate content.

IMPORTANT HTML TAGS AND COUNTS

Titles

No	Text
1	Tuatini's blog (2)

No	Text
1	[part 2] from deep learning papers implementation to s***pping models into production
2	[part 1] from deep learning papers implementation to s***pping models into production
3	Superdatascience podcast
4	Practical image segmentation with unet
5	Building tensorflow 1.3.0 as a standalone project (raspberry pi 3 included)
6	[part 2] how to setup your own environment for deep learning - for remote access
7	Pop****r tags

Text Styling

STRONG0
B0
EM0
I0
U0
CITE0

LINK ANALYSIS

Total Link Count: 48

Internal Link Count : 41

No	Text	Type
1	-	image
2	-	empty
3	-	empty
4	-	image
5	Tuatini's blog	text
6	-	empty
7	Home	text
8	About	text
9	Subscribe	text
10	RSS	text
11	Tuatini's blog	text
12	Home	text
13	About	text
14	Subscribe	text
15	[Part 2] From deep learning papers implementation to s*pping models into production While developing a product from scratch based on deep learning you always end up asking you this question: "How will I sp and maintain my deep learning models in production?". Given you are a data scientist or a deep learning researcher, maintaining deployed products is by far the&h***ip;	text
16	deep-learning	text
17	pytorch	text
18	paper	text
19	Comments	text
20	[Part 1] From deep learning papers implementation to s*pping models into production If you are a data scientist or a deep learning researcher, maintaining deployed products is by far the less exciting part of the process. In this guide, I'll show you how I managed to sp my image super-resolution project with minimal devops and maintenance.&h***ip;	text
21	deep-learning	text
22	pytorch	text
23	paper	text
24	Comments	text
25	SuperDatascience podcast Here is my in interview with Kirill Eremenko from SuperDatacience where we discuss various things around Data science/AI.&h****ip;	text
26	poadcast	text
27	superdatascience	text
28	Comments	text
29	Practical image segmentation with Unet Introduction In this post we will learn how Unet works, what it is used for and how to implement it. To do so we will use the original Unet paper, Pytorch and a Kaggle competion where Unet was maively used. If you don't know anything about Pytorch, you are afraid&h***ip;	text
30	deep-learning	text
31	pytorch	text
32	Comments	text
33	Building TensorFlow 1.3.0 as a standalone project (Raspberry pi 3 included) Here you'll learn how to build Tensorflow either for your x86_64 machine or for the raspberry pi 3 as a standalone shared library which can be interfaced from the C++ API. (This tutorial couldn't be possible without the help of the people from the References section) Watch out for&h****ip;	text
34	deep-learning	text
35	tensorflow	text
36	Comments	text
37	[Part 2] How to setup your own environment for deep learning - For remote access Introduction and goal In part 1 we learned how to setup your computer locally for deep learning. Now imagine you're like me and you usually work on your laptop but you still want to use your computer with a powerful GPU whenever you want from anywhere in the world, in&h****ip;	text
38	deep-learning	text
39	unix-sysadmin	text
40	Comments	text
41	Older Posts	text