TGViewer
Channel Public Channel
DataSpoof

DataSpoof

@dataspoof

Learn Data Science

https://dataspoof4081.graphy.com/membership

Artificial Intelligence
Machine Learning
Data Science
Deep learning
Computer vision
NLP
Big data
Subscribers
15.4K
Photos
239
Videos
22
Links
328

Showing posts older than #1022 · Back to latest

Older Posts 19 shown
Post #1021 6.11K
Wild! Google just announced that their quantum chip Willow was able to do a computation in 5 minutes that would take current top-tier computers 10,000,000,000,000,000,000,000,000 years to figure out 😳 The 105-qubit chip brings insane error correction, focusing on stability rather than just stacking more qubits. The result? A leap toward practical quantum computing that could revolutionize medicine, AI, and energy in the near future. But here comes the crazy part. As part of the Willow announcement, Google basically confirmed we're living in a multiverse: "It lends credence to the notion that quantum computation occurs in many parallel universes, in line with the idea that we live in a multiverse, a prediction first made by David Deutsch." What a time to be alive.


https://www.instagram.com/p/DDbE3U1yeDD/?igsh=MWFjOXc3ZWVqYTNwZw==
Post #1020 6.18K
Post #1012 9.27K
My Amazon SDE Interview experience for the reference of all freshers applying:

(FYI, Amazon just dropped their SDE-1 India University Graduate openings!)


⏳ The process:

1️⃣ 1 Online Assessment
2️⃣ 2 Coding rounds
3️⃣ 1 Coding + Leadership Principles round


💻 The interviews:

1️⃣ OA round:
7 basic code-debugging MCQs
2 DSA questions:
- LC 2265. Nodes Equal to Average of Subtree
- LC 68. Text Justification
1 (very lengthy) behavioral question form

- Solved all 7 debugging questions correctly.
- Solved first DSA problem in 10 mins.
- Partially solved second problem, failing few test cases.
- This round went average. But got the interview invite.

Being fast in contests and debugging would help in this round.


2️⃣ Coding round 1:
A BFS-based LeetCode hard problem.
- Quickly coded a BFS + hashmap solution.
- Interviewer had cross-questions but appeared satisfied overall.

If you can solve LC 127. Word Ladder, you’d be fine.


3️⃣ Coding round 2:
> Q1: Nodes at distance K in a binary tree
- Used BFS after creating parent pointers using HashMap.
> Q2: Connect ropes with minimum cost
- Implemented a greedy solution using a priority queue.
- Interviewer liked my speed but gave another problem.
> Q3: Max steps with reduced m
- Gave O(n) solution, then optimized using binary search to O(log n) and later to O(log(sqrt(m))).

Overall, pleasant interview with optimized solutions.

All of the above problems:
- LC 863. All Nodes Distance K in Binary Tree
- GFG. Connect n ropes with minimum cost
- Problem 3 not on the internet. Here’s a playground for it - https://lnkd.in/gsg2Pnmp


4️⃣ Coding + Managerial round:
- LC Hard; Smallest substring in ’s’ containing ’t’ as subsequence
- Came up with a sliding window approach.
- Took 30+ min to explain and code the approach.
- Interviewer was satisfied with my approach, but couldn’t finish coding completely.
- Overall, explained the concept but could have implemented faster.

If you have done LC 76. Minimum Window Substring, you got this one.

Behavioral Questions:
[1] Internship Discussion:
- Day-to-day responsibilities?
- Technologies you worked with, and why?
- Any accomplishments or key learnings?
[2] Amazon Leadership Principles:
- Time when you went above and beyond to meet a customer’s needs? (Customer Obsession)
- Time when you had to make a quick decision with limited information? (Bias for Action)

Decent answers in behavioral round as I had prepped for similar questions.


🎯 Result:
My interview result was positive and a few weeks later, I got the life-altering SDE-1 offer from Amazon

Credit- Harshit sharma
lnkd.in LinkedIn This link will take you to a page that’s not on LinkedIn
Post #1011 5.54K
Overfitting happens when a model learns too much detail from training data, including noise, rather than general patterns.

Result: The model performs well on training data but poorly on new, unseen data.

Symptoms: High accuracy on training data, low accuracy on test data.

Cause: Model is too complex (e.g., too many layers, features, or parameters).

Example: Memorizing answers for a specific test rather than understanding concepts.

Solution: Simplify the model, use regularization techniques, or gather more data.

Purpose of Avoiding Overfitting: Ensures the model can generalize and make accurate predictions on new data.
Post #1007 6.62K
We are launching a premium memberships for our followers

Currently it's contains AWS course for data scientist

Soon we will add following courses
1- Complete MLOPS
2- complete Data analyst
3- Complete ML engineer
4- Complete big data analyst

Support us by becoming a premium member and enjoys the benefits

https://dataspoof4081.graphy.com/membership
Post #998 5.96K
For any queries dm me on whatsapp
+9183182 38637
Older posts →
Threads Profile ViewerView any public Threads profile without an account.Open ThreadLook →Writing with AI? Make it sound human.Metric37 rewrites AI drafts so they read naturally. Free AI detector, 1,500 words free.Try Metric37 →