Tools & Wizards About Us
deeplearning.mit.edu screenshot

Report Summary

  • 62

    Performance

    Renders faster than
    76% of other websites

  • 77

    Accessibility

    Visual factors better than
    that of 44% of websites

  • 75

    Best Practices

    More advanced features
    available than in
    35% of websites

  • 93

    SEO

    Google-friendlier than
    83% of websites

deeplearning.mit.edu

MIT Deep Learning and Artificial Intelligence Lectures | Lex Fridman

Page Load Speed

891 ms in total

First Response

43 ms

Resources Loaded

651 ms

Page Rendered

197 ms

deeplearning.mit.edu screenshot

About Website

Click here to check amazing Deep Learning MIT content for United States. Otherwise, check out these important facts you probably never knew about deeplearning.mit.edu

A collection of lectures on deep learning, deep reinforcement learning, autonomous vehicles, and artificial intelligence organized by Lex Fridman.

Visit deeplearning.mit.edu

Key Findings

We analyzed Deeplearning.mit.edu page load time and found that the first response time was 43 ms and then it took 848 ms to load all DOM resources and completely render a web page. This is quite a good result, as only 15% of websites can load faster.

Performance Metrics

deeplearning.mit.edu performance score

62

Measured Metrics

name

value

score

weighting

FCP (First Contentful Paint)

Value2.9 s

53/100

10%

LCP (Largest Contentful Paint)

Value4.0 s

49/100

25%

SI (Speed Index)

Value3.2 s

92/100

10%

TBT (Total Blocking Time)

Value200 ms

90/100

30%

CLS (Cumulative Layout Shift)

Value0.429

22/100

15%

TTI (Time to Interactive)

Value7.1 s

51/100

10%

Network Requests Diagram

deeplearning.mit.edu

43 ms

deeplearning.mit.edu

137 ms

style.min.css

16 ms

style.css

47 ms

style.css

51 ms

Our browser made a total of 50 requests to load all elements on the main page. We found that 24% of them (12 requests) were addressed to the original Deeplearning.mit.edu, 58% (29 requests) were made to Lexfridman.com and 4% (2 requests) were made to Fonts.googleapis.com. The less responsive or slowest element that took the longest time to load (141 ms) relates to the external source Google-analytics.com.

Page Optimization Overview & Recommendations

Page size can be reduced by 74.2 kB (5%)

Content Size

1.4 MB

After Optimization

1.3 MB

  • HTML
  • Images
  • JavaScript
  • CSS

In fact, the total size of Deeplearning.mit.edu main page is 1.4 MB. This result falls beyond the top 1M of websites and identifies a large and not optimized web page that may take ages to load. 60% of websites need less resources to load. Images take 1.2 MB which makes up the majority of the site volume.

HTML Optimization

-89%

Potential reduce by 69.8 kB

  • Original 78.7 kB
  • After minification 59.6 kB
  • After compression 8.9 kB

HTML content can be minified and compressed by a website’s server. The most efficient way is to compress content using GZIP which reduces data amount travelling through the network between server and browser. This page needs HTML code to be minified as it can gain 19.1 kB, which is 24% of the original size. It is highly recommended that content of this web page should be compressed using GZIP, as it can save up to 69.8 kB or 89% of the original size.

Image Optimization

-0%

Potential reduce by 152 B

  • Original 1.2 MB
  • After minification 1.2 MB

Image size optimization can help to speed up a website loading time. The chart above shows the difference between the size before and after optimization. Deep Learning MIT images are well optimized though.

JavaScript Optimization

-0%

Potential reduce by 230 B

  • Original 56.9 kB
  • After minification 56.9 kB
  • After compression 56.7 kB

It’s better to minify JavaScript in order to improve website performance. The diagram shows the current total size of all JavaScript files against the prospective JavaScript size after its minification and compression. This website has mostly compressed JavaScripts.

CSS Optimization

-14%

Potential reduce by 4.0 kB

  • Original 28.7 kB
  • After minification 28.7 kB
  • After compression 24.7 kB

CSS files minification is very important to reduce a web page rendering time. The faster CSS files can load, the earlier a page can be rendered. Deeplearning.mit.edu needs all CSS files to be minified and compressed as it can save up to 4.0 kB or 14% of the original size.

Requests Breakdown

Number of requests can be reduced by 15 (34%)

Requests Now

44

After Optimization

29

  • AJAX
  • Images
  • JavaScript
  • CSS

The browser has sent 44 CSS, Javascripts, AJAX and image requests in order to completely render the main page of Deep Learning MIT. We recommend that multiple CSS and JavaScript files should be merged into one by each type, as it can help reduce assets requests from 7 to 1 for JavaScripts and from 10 to 1 for CSS and as a result speed up the page load time.

Network Requests Diagram

deeplearning.mit.edu

deeplearning.mit.edu

43 ms

deeplearning.mit.edu

deeplearning.mit.edu

137 ms

style.min.css

style.min.css

16 ms

style.css

style.css

47 ms

style.css

style.css

51 ms

font-open-sans.css

font-open-sans.css

52 ms

blocks.css

blocks.css

53 ms

jquery.min.js

jquery.min.js

79 ms

jquery-migrate.min.js

jquery-migrate.min.js

63 ms

navigation.js

navigation.js

64 ms

jquery-3.3.1.min.js

jquery-3.3.1.min.js

53 ms

css

css

52 ms

css

css

77 ms

style-grid-vid.css

style-grid-vid.css

85 ms

style-grid-current-lectures.css

style-grid-current-lectures.css

91 ms

style-team.css

style-team.css

92 ms

isotope.pkgd.min.js

isotope.pkgd.min.js

28 ms

analytics.js

analytics.js

141 ms

deep_learning_state_of_the_art_2020.png

deep_learning_state_of_the_art_2020.png

65 ms

andrew_trask_talk.png

andrew_trask_talk.png

64 ms

vivienne_sze_talk.png

vivienne_sze_talk.png

64 ms

vladimir_vapnik_talk.png

vladimir_vapnik_talk.png

90 ms

deep_learning_basics.png

deep_learning_basics.png

95 ms

deep_learning_state_of_the_art_2019.png

deep_learning_state_of_the_art_2019.png

96 ms

deep_rl_intro.png

deep_rl_intro.png

92 ms

self_driving_cars_2019.png

self_driving_cars_2019.png

91 ms

drago_anguelov.png

drago_anguelov.png

90 ms

oliver_cameron_talk.png

oliver_cameron_talk.png

97 ms

karl_iagnemma_talk.png

karl_iagnemma_talk.png

98 ms

self_driving_cars_2018.png

self_driving_cars_2018.png

95 ms

sacha_arnoud.png

sacha_arnoud.png

96 ms

emilio_frazzoli.png

emilio_frazzoli.png

100 ms

sterling_anderson_talk.png

sterling_anderson_talk.png

97 ms

chris_gerdes_talk.png

chris_gerdes_talk.png

99 ms

sertac_karaman_talk.png

sertac_karaman_talk.png

101 ms

deep_learning_2018.png

deep_learning_2018.png

98 ms

deep_reinforcement_learning_2018.png

deep_reinforcement_learning_2018.png

99 ms

computer_vision_2018.png

computer_vision_2018.png

100 ms

human_sensing_2018.png

human_sensing_2018.png

119 ms

deep_learning_self_driving_cars_2017.png

deep_learning_self_driving_cars_2017.png

119 ms

deep_reinforcement_learning_2017.png

deep_reinforcement_learning_2017.png

120 ms

convolutional_neural_networks_2017.png

convolutional_neural_networks_2017.png

119 ms

recurrent_neural_networks_2017.png

recurrent_neural_networks_2017.png

120 ms

human_sensing_2017.png

human_sensing_2017.png

120 ms

4iCs6KVjbNBYlgoKfw7w.woff

4iCs6KVjbNBYlgoKfw7w.woff

91 ms

open-sans-all-400-normal.woff

open-sans-all-400-normal.woff

56 ms

open-sans-all-700-normal.woff

open-sans-all-700-normal.woff

61 ms

1Ptxg8zYS_SKggPN4iEgvnHyvveLxVvaorCIPrc.woff

1Ptxg8zYS_SKggPN4iEgvnHyvveLxVvaorCIPrc.woff

93 ms

collect

collect

48 ms

js

js

78 ms

Accessibility Review

deeplearning.mit.edu accessibility score

77

Accessibility Issues

Contrast

These are opportunities to improve the legibility of your content.

Impact

Issue

High

Background and foreground colors do not have a sufficient contrast ratio.

Names and labels

These are opportunities to improve the semantics of the controls in your application. This may enhance the experience for users of assistive technology, like a screen reader.

Impact

Issue

High

Image elements do not have [alt] attributes

High

Links do not have a discernible name

Best Practices

deeplearning.mit.edu best practices score

75

Areas of Improvement

Trust and Safety

Impact

Issue

High

Does not use HTTPS

High

Includes front-end JavaScript libraries with known security vulnerabilities

Low

Ensure CSP is effective against XSS attacks

User Experience

Impact

Issue

High

Serves images with low resolution

General

Impact

Issue

Low

Detected JavaScript libraries

SEO Factors

deeplearning.mit.edu SEO score

93

Search Engine Optimization Advices

Content Best Practices

Format your HTML in a way that enables crawlers to better understand your app’s content.

Impact

Issue

High

Image elements do not have [alt] attributes

Mobile Friendly

Make sure your pages are mobile friendly so users don’t have to pinch or zoom in order to read the content pages. [Learn more](https://developers.google.com/search/mobile-sites/).

Impact

Issue

High

Document uses legible font sizes

Language and Encoding

  • Language Detected

    EN

  • Language Claimed

    EN

  • Encoding

    UTF-8

Language claimed in HTML meta tag should match the language actually used on the web page. Otherwise Deeplearning.mit.edu can be misinterpreted by Google and other search engines. Our service has detected that English is used on the page, and it matches the claimed language. Our system also found out that Deeplearning.mit.edu main page’s claimed encoding is utf-8. Use of this encoding format is the best practice as the main page visitors from all over the world won’t have any issues with symbol transcription.

Social Sharing Optimization

deeplearning.mit.edu

Open Graph data is detected on the main page of Deep Learning MIT. This is the best way to make the web page social media friendly. Here is how it looks like on Facebook: