Neo

To die by your thigh
Well, the pleasure, the privilege is mine

[Neo @Stupendous011]

@TopGyaru @dey_twts

[Neo @Stupendous011]

My favourite Bengali novelist is Sarat Chandra Chattopadhyay.
Favourite novelistess? Anila Devi 😌

[Neo @Stupendous011]

‘Fresh samosa,’ he said

[Neo @Stupendous011]

@RiddleSphere @IndianTechGuide Most chatgpt comment ever...what was the prompt ?
Make it sound genz???

[Roberto @aboynamedposh]

Can a 55 year old woman really consent to this?

[Neo @Stupendous011]

IS DT alright ?

[vixhaℓ @TheVixhal]

Today, the warden of the girls hostel suddenly came into my girlfriend's room… and the worst part was that I was in my girlfriend's room 😭.

Now imagine the scene:
- Warden banging on the door.
- My girlfriend panicking.

Me standing there like, "Bro, this is how my college journey ends."

The warden starts interrogating me:
- What are you doing here?
- Where is your ID card?
- Are you really her 'cousin'?

Each question felt like a mini-death penalty. I knew one wrong answer and my entire semester GPA would be replaced by an FIR number.

And that's basically what Bayes Theorem does: It's the warden of probability: interrogating our assumptions with evidence, and updating beliefs step by step.

Bayes Theorem is nothing but just a mathematical way to update your beliefs when you see new evidence.

Formula:
P(H | E) = (P(E | H) × P(H)) / P(E)

Where:
- H: Hypothesis
- E: Event (what you observe)
- P(H): Prior probability
- P(E): Overall probability of event
- P(E|H): Prob. of event if hypothesis was true
- P(H|E): Probability hypothesis is true given event (updated belief)

Let's take an example:

In a neighborhood, 90% of children were falling sick due to flu and 10% due to measles (no other diseases). The probability of observing rashes for measles is 0.95 and for flu is 0.08. If a child develops rashes, find the probability that the child has flu.

Let's solve step by step:

Step 1: Define Hypotheses
- H1: The child has flu
- H2: The child has measles

Step 2: Define Event
- Event (E) = Child has rashes

Step 3: Write Priors
- P(H1) = 0.9 (90% children have flu)
- P(H2) = 0.1 (10% children have measles)

Step 4: Calculate Likelihoods
- P(E|H1) = 0.08 (rash probability if flu)
- P(E|H2) = 0.95 (rash probability if measles)

Step 5: Calculate Total Probability of Rashes
- By law of total probability:
- P(E) = P(E|H1)•P(H1) + P(E|H2)•P(H2)
- P(E) = (0.08)•(0.9) + (0.95)•(0.1)
- P(E) = 0.072 + 0.095
- P(E) = 0.167

So overall, 16.7% of children develop rashes.

Step 6: Apply Bayes Theorem
- P(H1 | E) = (P(E | H1) × P(H1)) / P(E)
- P(H1 | E) = ((0.08) × (0.9)) / 0.167
- P(H1 | E) = 0.431

Final Answer:

If a child has rashes, the probability they have flu = 43.1%.

Congratulations 🎉, you've just learned Bayes Theorem!

Bonus: Applications of Bayes Theorem in AI/ML

1. Recommendation Systems:
Netflix doesn't just recommend based on genre. It uses Bayes Theorem: "Given that this person watched 5 horror movies, what's the probability they'll like this thriller?" It updates recommendations as you watch more content.

2. Naive Bayes Classifier:
One of the easiest yet surprisingly powerful ML algorithms. It assumes features are independent (naive assumption) and uses Bayes Theorem to classify things like:
- Spam vs. Ham emails
- Sentiment analysis
- Document categorization

3. Advanced Applications:
Once you understand the basics, Bayes is behind many advanced techniques:

Hidden Markov Models (HMMs): For speech recognition, part-of-speech tagging

Expectation-Maximization (EM): For Gaussian Mixture Models, handling missing data

Bayesian Optimization: Efficient hyperparameter tuning for ML models

Bayes Theorem is the mathematical foundation for handling uncertainty in machine learning. Every time an algorithm needs to update its beliefs based on new evidence, Bayes Theorem is working behind the scenes!

[Neo @Stupendous011]

Godzilla grunts...
Mothra moans.

[Neo @Stupendous011]

Geez... I open twitter comments to read comments ... Not others spamming their contents

[Neo @Stupendous011]

this Delhi dog thing makes me think...
cat lobbing is soo strong ...ANd no way they did CAA on dogs... Cat-rastra in the making...less goo.

[John Slaughter, Esq. @JSlaughterEsq]

I’ve always been clever enough to get by without effort. Over time, talent became a crutch, and the laziness it bred kept me from ever striving or achieving more.

[xoup @xoupgone]

@cornski Nut Like Us

[Neo @Stupendous011]

@IPL2025Auction @grok Not interested

[Neo @Stupendous011]

@nothing @grok Yeah ig 16k won't be it

[Neo @Stupendous011]

@nothingindia @grok Nothing Phone 4 .

[Neo @Stupendous011]

We’re experiencing an outage.

Thanks for your patience while we get our ducks in a row!

----never saw a search engine facing problems...
DUCK DUCK crashed

[Neo @Stupendous011]

@cneuralnetwork @pixieperceptron bro didnt just her her a goat

[Neo @Stupendous011]

@conciliateme Achaaa

[Neo @Stupendous011]

@dey_twts MIT published a paper on this

[Neo @Stupendous011]

@AnshumatVakra Nice... Keep it white