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What you know today will be outdated tomorrow.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
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
On libraries, algorithms, and tools.
(If you want to start with machine learning, having a comprehensive set of hands-on tutorials you can always refer to is fundamental.)
🧵👇
1⃣ Notebooks are a fantastic way to code, experiment, and communicate your results.
Take a look at @CoreyMSchafer's fantastic 30-minute tutorial on Jupyter Notebooks.
https://t.co/HqE9yt8TkB
2⃣ The Pandas library is the gold-standard to manipulate structured data.
Check out @joejamesusa's "Pandas Tutorial. Intro to DataFrames."
https://t.co/aOLh0dcGF5
3⃣ Data visualization is key for anyone practicing machine learning.
Check out @blondiebytes's "Learn Matplotlib in 6 minutes" tutorial.
https://t.co/QxjsODI1HB
4⃣ Another trendy data visualization library is Seaborn.
@NewThinkTank put together "Seaborn Tutorial 2020," which I highly recommend.
https://t.co/eAU5NBucbm
More from Tech
@MadhusudanKela @VQIndia @sameervq
My key learnings: ⬇️⬇️⬇️
Bubble or Bull Market? Join us for a short presentation and candid one on one on 27th Jan, 4pm with Shri \u2066@MadhusudanKela\u2069. \u2066@VQIndia\u2069 \u2066@sameervq\u2069 #bubbleorbullmarket pic.twitter.com/LBvlBrz6mS
— Ravi Dharamshi (@ravidharamshi77) January 24, 2021
First, the BEAR case:
1. Bitcoin has surpassed all the bubbles of the last 45 years in extent that includes Gold, Nikkei, dotcom bubble.
2. Cyclically adjusted PE ratio for S&P 500 almost at 1929 (The Great Depression) peaks, at highest levels except the dotcom crisis in 2000.
3. World market cap to GDP ratio presently at 124% vs last 5 years average of 92% & last 10 years average of 85%.
US market cap to GDP nearing 200%.
4. Bitcoin (as an asset class) has moved to the 3rd place in terms of price gains in preceding 3 years before peak (900%); 1st was Tulip bubble in 17th century (rising 2200%).
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@NBA @StephenKissler @yhgrad 1. From Day 1, SARS-COV-2 was very well adapted to humans .....and transgenic hACE2 Mice
1. From Day 1, SARS-COV-2 was very well adapted to humans .....and transgenic hACE2 Mice
— Billy Bostickson \U0001f3f4\U0001f441&\U0001f441 \U0001f193 (@BillyBostickson) January 30, 2021
"we generated a mouse model expressing hACE2 by using CRISPR/Cas9 knockin technology. In comparison with wild-type C57BL/6 mice, both young & aged hACE2 mice sustained high viral loads... pic.twitter.com/j94XtSkscj
@NBA @StephenKissler @yhgrad 2. High Probability of serial passaging in Transgenic Mice expressing hACE2 in genesis of SARS-COV-2
1. High Probability of serial passaging in Transgenic Mice expressing hACE2 in genesis of SARS-COV-2!
— Billy Bostickson \U0001f3f4\U0001f441&\U0001f441 \U0001f193 (@BillyBostickson) January 2, 2021
2 papers:
Human\u2013viral molecular mimicryhttps://t.co/irfH0Zgrve
Molecular Mimicryhttps://t.co/yLQoUtfS6s https://t.co/lsCv2iMEQz
@NBA @StephenKissler @yhgrad B.1.1.7 has an unusually large number of genetic changes, ... found to date in mouse-adapted SARS-CoV2 and is also seen in ferret infections.
https://t.co/9Z4oJmkcKj
@NBA @StephenKissler @yhgrad We adapted a clinical isolate of SARS-CoV-2 by serial passaging in the ... Thus, this mouse-adapted strain and associated challenge model should be ... (B) SARS-CoV-2 genomic RNA loads in mouse lung homogenates at P0 to P6.
https://t.co/I90OOCJg7o
Please add your own.
2/ The Magic Question: "What would need to be true for you
1/\u201cWhat would need to be true for you to\u2026.X\u201d
— Erik Torenberg (@eriktorenberg) December 4, 2018
Why is this the most powerful question you can ask when attempting to reach an agreement with another human being or organization?
A thread, co-written by @deanmbrody: https://t.co/Yo6jHbSit9
3/ On evaluating where someone’s head is at regarding a topic they are being wishy-washy about or delaying.
“Gun to the head—what would you decide now?”
“Fast forward 6 months after your sabbatical--how would you decide: what criteria is most important to you?”
4/ Other Q’s re: decisions:
“Putting aside a list of pros/cons, what’s the *one* reason you’re doing this?” “Why is that the most important reason?”
“What’s end-game here?”
“What does success look like in a world where you pick that path?”
5/ When listening, after empathizing, and wanting to help them make their own decisions without imposing your world view:
“What would the best version of yourself do”?