harpreet Profile picture
Jan 3, 2023 โ€ข 13 tweets โ€ข 7 min read โ€ข Read on X
๐Ÿคฏ Say goodbye to lifeless textbooks and hello to an exciting way to learn statistics! ๐Ÿ’ช

I have a masters degree in statistics, but these 11 books taught me more about how statistics in the real world than any course I've taken.

Have you read any of them?๐Ÿ‘‡๐Ÿฝ๐Ÿงต

#66DaysOfData
๐Ÿ“š๐Ÿง Who says learning statistics has to be boring?!

๐Ÿค“ The Manga Guide to Statistics makes it fun and easy to learn all the basic concepts, with entertaining examples and applications.

Get it here:nostarch.com/mg_statistics.โ€ฆ
Learn to calculate regression equations and perform hypothesis tests with The Manga Guide to Regression Analysis.

You also learn: simple, multiple, and logistic regression to predict iced tea orders and bakery revenues, and calculate confidence intervals and odds ratios.
๐Ÿง Introduces you to the tools of data analysis like graphs, charts, and tables, and exploring how to use samples to answer questions.

๐Ÿ’ก Plus, the author covers common data collection problems like selection bias and measurement error and how to deal with them effectively.
Naked statistics is a classic and bound to be on any and every list of statistics books.

It's a great read with a number of real world case studies.

IMO, not as good as The Art of Statistics.

Get a FREE pdf here: penguinrandomhouse.ca/books/404461/nโ€ฆ
๐Ÿ“ˆ๐Ÿ”ฎWhy are so many predictions wrong?

๐Ÿค”The Signal And The Noise breaks it down: sometimes we get overwhelmed by masses of data and forget to be cautious and diligent in finding the important signals.

Get a FREE pdf here: forecastwatch.com/wp-content/uplโ€ฆ
๐Ÿง๐Ÿ’กHow to Measure Anything teaches you how to make smart decisions using applied logic and behavioral economics.

๐Ÿคฏ Discover how misframing what needs to be measured and misperception of measurement elements can lead to mismeasurement and perceptions of the immeasurable.
You won't believe the history behind Bayes' rule: discoverd in 1740s by amateur mathematician to solving WWII codebreaking and now used in DNA decoding and Homeland Security.

๐Ÿ”๐Ÿ’ป The Theory That Would Not Die explores the controversy and obsessions surrounding this theorem. ๐Ÿ”ฅ
How Not To Be Wrong will help you make better decisions, navigate life effortlessly, and assess risks like a pro.

๐Ÿ’ช๐Ÿฝ Here are 3 key lessons to get you started:
1) Math is mostly common sense.
2) Probability โ‰  risk.
3) Scientific research findings can be wrong.

๐Ÿค“
๐Ÿค” "The Success Equation" helps untangle the intricate strands of skill and luck in our lives and offers concrete tips on how to use this knowledge to make better decisions.

๐Ÿ’ช๐Ÿฝ Don't miss out on this must-read for anyone looking to succeed in business and life.
๐ŸŽฒ "Thinking in Bets" shows you how to objectively evaluate your beliefs, work around biases, and learn from the past.

๐Ÿ’ช๐Ÿฝ Every decision is a bet, and this guide will help you navigate the quantifiable risks and come out on top.
๐Ÿ’ฅ๐Ÿค” Fooled by Randomness uncovers the role of chance in business and investing, and how it influences our actions, decisions, and risk-taking.

๐Ÿคฏ Get a fresh perspective on the role of uncertainty in our world. ๐ŸŒ€
Which of these have you read?

Which do you recommend?

1. Follow me @DataScienceHarp for more of these
2. RT the tweet below & share w/ your friends
3. For SQL follow: @nevrekaraishwa2
4. For Python and ML follow: @SanthoshKumarS_ @GiftOjeabulu_ @Sumanth_077 @Saboo_Shubham_

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More from @DataScienceHarp

Jan 6, 2023
Skip connections are one of the most important innovations in the history of deep learning.

You need to understand the problems it solves and how it helps us build deep networks.

Let me break it down for you in this thread ๐Ÿ‘‡๐Ÿฝ๐Ÿงต

#deeplearning #machinelearning
Skip connections are a common feature in modern CNN architectures. They create an alternative path for the gradient to flow through, which can help the model learn faster.
In a neural network, the gradient measures how much a change in one part of the network affects the output. We use the gradient to update the network during training to recognize data patterns better.
Read 8 tweets
Jan 6, 2023
The hardest part of data science is keeping track of and measuring the right things.

And you do that with metrics.

Most data scientists don't know what makes a good metric.

In this thread, I'll teach you what makes a good metric๐Ÿ“ˆ
A good metric has four qualities; it's...
1) Comparative
2) Understandable
3) A ratio or rate
4) Changes in behaviour
1) A good metric is comparative

Comparing a metric to other periods, groups of users or competitors helps you understand which way things are moving.
Read 9 tweets
Jan 6, 2023
The convolution operation q fundamental concepts in deep learning.

Most people know how to perform it.

But far fewer understand what it means.

These five ๐Ÿ–๐Ÿฝ resources will help you grok one of the most important concepts in deep learning ๐Ÿ‘‡๐Ÿฝ

#deeplearning #machinelearning
1) Convolution vs cross correlation

You must understand the difference between convolution and cross-correlation in order to understand backpropagation in CNNs.

This will help.

glassboxmedicine.com/2019/07/26/conโ€ฆ
2) Convolutions in image processing

This 30ish minute lecture is from the MIT course on computational thinking.

Oh, and itโ€™s taught by Grant from @3blue1brown

Read 7 tweets
Jan 5, 2023
Accuracy, precision, recall, and F1-score are a few ways you can measure the performance of your classification model.

Most people get them confused.

Use this thread as a cheat sheet and remind yourself what they mean๐Ÿ‘‡๐Ÿฝ๐Ÿงต
Accuracy: This is the number of correct predictions made divided by the total number of predictions.
True/false positives/negatives: For binary classification, these refer to correctly/incorrectly predicted positive/negative class samples.
Read 10 tweets
Jan 5, 2023
Here's how I would study deep learning if I had to do it all over again.

#deeplearning #machinelearning
๐Ÿ‘‡๐Ÿฝ ๐Ÿงต
1) Skip the math

Iโ€™d ignore the math when first starting out.

Looking at equations will demotivate you.

Instead, look for applications of deep learning.

Clone the
@ultralytics
Yolov5 repo and run the usage on the command line.

See the magic happen.

Get inspired.
2) Go through @AndrewGlassnerโ€™s DL crash course.

Itโ€™s 3.5 hours long but will give you an intuition for how it all works under the hood.

Great return on time investment.

Read 12 tweets
Jan 4, 2023
Back propagation is the secret sauce for training neural networks.

You need to understand how it works.

I break it down for you in this thread ๐Ÿ‘‡๐Ÿฝ๐Ÿงต

#deeplearning
Here's the lowdown: the output of a neural network is calculated with the ๐Ÿ‹๐Ÿฝ weights ๐Ÿ‹๐Ÿฝ of the edges that connect the nodes in the network.

So, you gotta find the optimal values of weights to minimize the final error on training data examples.
1๏ธโƒฃ We start by assigning random values to all weights in the network

2๏ธโƒฃ Then, for every input sample, we perform a feedforward operation to calculate the final output and the prediction error.
Read 10 tweets

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