Thread for beginners; How to learn machine learning.
Naturally, decision trees make an intuitive sense for learners. While you learn decision trees I highly recommend the Titanic survival prediction problem. It is something you can relate to which is quite useful for learning something new
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Two questions. 1/ Does this summarise the AZ published data :
The plan is to extend the time interval for all age groups despite it being largely untested on the over 55yrs, although the full data is not yet published
SUMMARY: the Oxford/Astra trial examined dosing with gaps between 4-12 wks- although longer gaps appear to be limited mostly to younger participants. There was no difference reported in published data between these & efficacy from the 1st dose seems high for severe disease.
— Deepti Gurdasani (@dgurdasani1) December 31, 2020
Do we have the actual numbers of over 55yr olds given a 2nd dose at c12 weeks and the accompanying efficacy data?
Not to mention the efficacy data of the full first dose over that same period?
I’d quite like to know whether I am to be a guinea pig & the ongoing risks to manage
You attached photos of excerpts from a paper. Could you attach the link?
Re Pfizer. As I understand it the most efficacious interval for dosing was investigated at the start of the trial.
Discussions of 1 vs 2 doses suggest many are not aware of Pfizer's trials which evaluated 1 vs 2 dose immunogenicity, assessed multiple formulations (BNT162b1 BNT162b2 etc) & conducted dose-ranging in both young & old adults at the start. Saw "clear benefit of booster at day 21" pic.twitter.com/mpyxu9xFSF
— Dr Nicole E Basta (@IDEpiPhD) December 31, 2020
Here’s the link to the
I’ve got to say that this way of making and announcing decisions is not inspiring confidence in me and I am very pro vaccination as a matter of principle, not least because my brother caught polio before vaccinations available.
This thread is for you.
🧵👇
The guide that you will see below is based on resources that I came across, and some of my experiences over the past 2 years or so.
I use these resources and they will (hopefully) help you in understanding the theoretical aspects of machine learning very well.
Before diving into maths, I suggest first having solid programming skills in Python.
Read this thread for more
Are you planning to learn Python for machine learning this year?
— Pratham Prasoon (@PrasoonPratham) February 13, 2021
Here's everything you need to get started.
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These are topics of math you'll have to focus on for machine learning👇
- Trigonometry & Algebra
These are the main pre-requisites for other topics on this list.
(There are other pre-requites but these are the most common)
- Linear Algebra
To manipulate and represent data.
- Calculus
To train and optimize your machine learning model, this is very important.
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As someone\u2019s who\u2019s read the book, this review strikes me as tremendously unfair. It mostly faults Adler for not writing the book the reviewer wishes he had! https://t.co/pqpt5Ziivj
— Teresa M. Bejan (@tmbejan) January 12, 2021
The meat of the criticism is that the history Adler gives is insufficiently critical. Adler describes a few figures who had a great influence on how the modern US university was formed. It's certainly critical: it focuses on the social Darwinism of these figures. 2/x
Other insinuations and suggestions in the review seem wildly off the mark, distorted, or inappropriate-- for example, that the book is clickbaity (it is scholarly) or conservative (hardly) or connected to the events at the Capitol (give me a break). 3/x
The core question: in what sense is classics inherently racist? Classics is old. On Adler's account, it begins in ancient Rome and is revived in the Renaissance. Slavery (Christiansen's primary concern) is also very old. Let's say classics is an education for slaveowners. 4/x
It's worth remembering that literacy itself is elite throughout most of this history. Literacy is, then, also the education of slaveowners. We can honor oral and musical traditions without denying that literacy is, generally, good. 5/x