🔗 Cheat sheets for Machine Learning and Data Science interviews
Convenient to review before the interview or to brush up your basics.
A developer posted a useful set of cheat sheets for interview preparation. They contain all the most important information on key ML and DS topics.
Convenient to review before the interview or to brush up your basics.
🔗 Machine Learning Roadmap
1️⃣ Build a solid foundation in math and stats
2️⃣ Dive into ML algorithms like Linear Regression, SVM, and Clustering
3️⃣ Choose your ML focus, from supervised learning to recommender systems
4️⃣ Master popular libraries like PyTorch, TensorFlow, and Scikit-learn
5️⃣ Gain real-world experience with projects and side gigs
Whether you're just starting out or looking to refine your skills, this Machine Learning Roadmap breaks down every step
1️⃣ Build a solid foundation in math and stats
2️⃣ Dive into ML algorithms like Linear Regression, SVM, and Clustering
3️⃣ Choose your ML focus, from supervised learning to recommender systems
4️⃣ Master popular libraries like PyTorch, TensorFlow, and Scikit-learn
5️⃣ Gain real-world experience with projects and side gigs
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🚀 Want to speed up training in PyTorch by several times?
DataLoader has two bad defaults that slow down the process.
By fixing them, I got almost 5x speedup.
❌ Problem
-
- While the GPU is computing, the CPU does nothing.
- While the CPU prepares data, the GPU is idle.
⚡️ Solution
You need to make the CPU and GPU work in parallel:
- In
- When transferring data, use
- Add
✅ As a result, the CPU prepares the next batch while the GPU is busy with the current one.
This eliminates idle time, and training goes noticeably faster.
DataLoader has two bad defaults that slow down the process.
By fixing them, I got almost 5x speedup.
❌ Problem
-
.to(device) transfers data to the GPU. - While the GPU is computing, the CPU does nothing.
- While the CPU prepares data, the GPU is idle.
⚡️ Solution
You need to make the CPU and GPU work in parallel:
- In
DataLoader, set pin_memory=True - When transferring data, use
.to(device, non_blocking=True) - Add
num_workers to DataLoader for background loading. ✅ As a result, the CPU prepares the next batch while the GPU is busy with the current one.
This eliminates idle time, and training goes noticeably faster.
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•Step 2- Then Scroll Or Search System Setting & Open Tumhara Kisi Aur Name Se Ho Sakhta Hein
•Step 3- Then Click Multiple Users Tumhara Ye BHI Kisi Aur NAME SE Ho Sakhta Hein
•Step 4- Waha Tumhe Apne Phone Ka Owner Milega Jo Tum Ho & Waha Ek Add User / Guest Ka Option Milega Waha Click Karke ADD Karlena Sari Permission Allow Karke
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❤1
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