We aren't doing this near enough.
Some of the things I've learned in more than 20 years in the tech industry.
You need to hear these.
🧵👇
We aren't doing this near enough.
No small improvement is too small.
Just aim for something new every day, and you'll be surprised at the end.
Be the person that pulls everyone out of the rabbit holes.
Great results will get you farther than processes, but good processes can help you achieve good results.
It's funny how everything you share finds a way to reward you back.
We all make mistakes. Move on from them and focus on what's coming.
Ask away!
(There are, however, stupid people with fragile egos that get bothered when others ask. Ignore them.)
Embrace change.
People fantasize about perfection, but perfectionism rarely wins.
Shipping more often will give you better odds than gilding the lily.
What you know today will be outdated tomorrow.
Make a plan to keep up and follow it... or you'll get behind.
(And it looks horrible in your resume.)
More from Santiago
You gotta think about this one carefully!
Imagine you go to the doctor and get tested for a rare disease (only 1 in 10,000 people get it.)
The test is 99% effective in detecting both sick and healthy people.
Your test comes back positive.
Are you really sick? Explain below 👇
The most complete answer from every reply so far is from Dr. Lena. Thanks for taking the time and going through
You can get the answer using Bayes' theorem, but let's try to come up with it in a different —maybe more intuitive— way.
👇
Here is what we know:
- Out of 10,000 people, 1 is sick
- Out of 100 sick people, 99 test positive
- Out of 100 healthy people, 99 test negative
Assuming 1 million people take the test (including you):
- 100 of them are sick
- 999,900 of them are healthy
👇
Let's now test both groups, starting with the 100 people sick:
▫️ 99 of them will be diagnosed (correctly) as sick (99%)
▫️ 1 of them is going to be diagnosed (incorrectly) as healthy (1%)
👇
Imagine you go to the doctor and get tested for a rare disease (only 1 in 10,000 people get it.)
The test is 99% effective in detecting both sick and healthy people.
Your test comes back positive.
Are you really sick? Explain below 👇
The most complete answer from every reply so far is from Dr. Lena. Thanks for taking the time and going through
Really doesn\u2019t fit well in a tweet. pic.twitter.com/xN0pAyniFS
— Dr. Lena Sugar \U0001f3f3\ufe0f\u200d\U0001f308\U0001f1ea\U0001f1fa\U0001f1ef\U0001f1f5 (@_jvs) February 18, 2021
You can get the answer using Bayes' theorem, but let's try to come up with it in a different —maybe more intuitive— way.
👇
Here is what we know:
- Out of 10,000 people, 1 is sick
- Out of 100 sick people, 99 test positive
- Out of 100 healthy people, 99 test negative
Assuming 1 million people take the test (including you):
- 100 of them are sick
- 999,900 of them are healthy
👇
Let's now test both groups, starting with the 100 people sick:
▫️ 99 of them will be diagnosed (correctly) as sick (99%)
▫️ 1 of them is going to be diagnosed (incorrectly) as healthy (1%)
👇
Free machine learning education.
Many top universities are making their Machine Learning and Deep Learning programs publicly available. All of this information is now online and free for everyone!
Here are 6 of these programs. Pick one and get started!
↓
Introduction to Deep Learning
MIT Course 6.S191
Alexander Amini and Ava Soleimany
Introductory course on deep learning methods and practical experience using TensorFlow. Covers applications to computer vision, natural language processing, and more.
https://t.co/Uxx97WPCfR
Deep Learning
NYU DS-GA 1008
Yann LeCun and Alfredo Canziani
This course covers the latest techniques in deep learning and representation learning with applications to computer vision, natural language understanding, and speech recognition.
https://t.co/cKzpDOBVl1
Designing, Visualizing, and Understanding Deep Neural Networks
UC Berkeley CS L182
John Canny
A theoretical course focusing on design principles and best practices to design deep neural networks.
https://t.co/1TFUAIrAKb
Applied Machine Learning
Cornell Tech CS 5787
Volodymyr Kuleshov
A machine learning introductory course that starts from the very basics, covering all of the most important machine learning algorithms and how to apply them in practice.
https://t.co/hD5no8Pdfa
Many top universities are making their Machine Learning and Deep Learning programs publicly available. All of this information is now online and free for everyone!
Here are 6 of these programs. Pick one and get started!
↓
Introduction to Deep Learning
MIT Course 6.S191
Alexander Amini and Ava Soleimany
Introductory course on deep learning methods and practical experience using TensorFlow. Covers applications to computer vision, natural language processing, and more.
https://t.co/Uxx97WPCfR
Deep Learning
NYU DS-GA 1008
Yann LeCun and Alfredo Canziani
This course covers the latest techniques in deep learning and representation learning with applications to computer vision, natural language understanding, and speech recognition.
https://t.co/cKzpDOBVl1
Designing, Visualizing, and Understanding Deep Neural Networks
UC Berkeley CS L182
John Canny
A theoretical course focusing on design principles and best practices to design deep neural networks.
https://t.co/1TFUAIrAKb
Applied Machine Learning
Cornell Tech CS 5787
Volodymyr Kuleshov
A machine learning introductory course that starts from the very basics, covering all of the most important machine learning algorithms and how to apply them in practice.
https://t.co/hD5no8Pdfa
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The first area to focus on is diversity. This has become a dogma in the tech world, and despite the fact that tech is one of the most meritocratic industries in the world, there are constant efforts to promote diversity at the expense of fairness, merit and competency. Examples:
USC's Interactive Media & Games Division cancels all-star panel that included top-tier game developers who were invited to share their experiences with students. Why? Because there were no women on the
ElectronConf is a conf which chooses presenters based on blind auditions; the identity, gender, and race of the speaker is not known to the selection team. The results of that merit-based approach was an all-male panel. So they cancelled the conference.
Apple's head of diversity (a black woman) got in trouble for promoting a vision of diversity that is at odds with contemporary progressive dogma. (She left the company shortly after this
Also in the name of diversity, there is unabashed discrimination against men (especially white men) in tech, in both hiring policies and in other arenas. One such example is this, a developer workshop that specifically excluded men: https://t.co/N0SkH4hR35
USC's Interactive Media & Games Division cancels all-star panel that included top-tier game developers who were invited to share their experiences with students. Why? Because there were no women on the
ElectronConf is a conf which chooses presenters based on blind auditions; the identity, gender, and race of the speaker is not known to the selection team. The results of that merit-based approach was an all-male panel. So they cancelled the conference.
Apple's head of diversity (a black woman) got in trouble for promoting a vision of diversity that is at odds with contemporary progressive dogma. (She left the company shortly after this
Also in the name of diversity, there is unabashed discrimination against men (especially white men) in tech, in both hiring policies and in other arenas. One such example is this, a developer workshop that specifically excluded men: https://t.co/N0SkH4hR35