❔ Interview question

What is the difference between using tensor.detach() and wrapping code in with torch.no_grad()?

Answer: with torch.no_grad() is a context manager that globally disables gradient calculation for all operations within its block. It's used during inference to reduce memory usage and speed up computation. tensor.detach() is a tensor-specific method that creates a new tensor sharing the same data but detached from the current computation graph. This stops gradients from flowing back to the original graph through this tensor, effectively creating a fork.

tags: #interview #pytorch #machinelearning

➡ @DataScienceQ

❔ Interview question

When saving a PyTorch model, what is the difference between saving the entire model versus saving just the model's state_dict? Which approach is generally recommended and why?

Answer: Saving the entire model (torch.save(model, PATH)) pickles the entire Python object, including the model architecture and its parameters. Saving just the state_dict (torch.save(model.state_dict(), PATH)) saves only a dictionary of the model's parameters (weights and biases).

The recommended approach is to save the state_dict because it is more flexible and robust. It decouples the saved weights from the specific code that defined the model, making your code easier to refactor and share without breaking the loading process.

tags: #interview #pytorch #machinelearning

➡ @DataScienceQ

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By: @DataScienceQ ✨

❔ Interview question

What is the purpose of a pooling layer in a Convolutional Neural Network (CNN)?

Answer: A pooling layer (like Max Pooling or Average Pooling) is used to progressively reduce the spatial size (width and height) of the feature maps. This serves two main purposes: 1) It reduces the number of parameters and computational complexity, which helps to control overfitting. 2) It introduces a degree of translation invariance, meaning the network becomes more robust to small shifts and distortions in the position of features in the input image.

tags: #interview #cnn #deeplearning

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By: @DataScienceQ ✨

❔ Interview question

What is the difference between the == (loose comparison) and === (strict comparison) operators in PHP?

Answer: The == operator is for loose comparison, checking for value equality after type juggling. For example, 1 == "1" is true. The === operator is for strict comparison, checking for both value AND type equality, without any type conversion. So, 1 === "1" is false. It's generally safer to use === to avoid unexpected bugs.

tags: #interview #php

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By: @DataScienceQ ✨

❔ Interview question

What is the difference between isset() and empty() in PHP?

Answer: isset() returns true only if a variable is declared and is not NULL. In contrast, empty() returns true if a variable is considered "falsy", which includes NULL, false, 0, "0", an empty string "", or an empty array. A key difference is that a variable like $var = 0; is set (so isset() is true), but also considered empty (so empty() is true).

tags: #interview #php

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By: @DataScienceQ ✨

❔ Interview question

Why is it better to use os.path.join() to construct paths instead of simple string concatenation?

Answer: Because os.path.join() handles cross-platform compatibility automatically. Operating systems use different path separators (e.g., / for Linux/macOS and \ for Windows). Hardcoding a separator like 'folder' + '/' + 'file' will break on a different OS. os.path.join('folder', 'file') correctly produces folder/file or folder\file depending on the system, making the code robust and portable.

tags: #interview #python #os

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By: @DataScienceQ ✨

❔ Interview question

When would you use the __slots__ attribute in a Python class, and what is its main trade-off?

Answer: The __slots__ attribute is used for memory optimization. By defining it in a class, you prevent the creation of a __dict__ for each instance, instead allocating a fixed amount of space for the specified attributes. This is highly effective when creating a large number of objects. The primary trade-off is that you lose the ability to add new attributes to instances at runtime.

tags: #python #interview

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By: @DataScienceQ ✨

❔ Interview Question

What is the GIL (Global Interpreter Lock) in Python, and how does it impact the execution of multi-threaded programs?

Answer: The Global Interpreter Lock (GIL) is a mutex (or a lock) that allows only one thread to hold the control of the Python interpreter at any one time. This means that in a CPython process, only one thread can be executing Python bytecode at any given moment, even on a multi-core processor.

This has a significant impact on performance:

• For CPU-bound tasks: Multi-threaded Python programs see no performance gain from multiple CPU cores. If you have a task that performs heavy calculations (e.g., image processing, complex math), creating multiple threads will not make it run faster. The threads will execute sequentially, not in parallel, because they have to take turns acquiring the GIL.

• For I/O-bound tasks: The GIL is less of a problem. When a thread is waiting for Input/Output (I/O) operations (like waiting for a network response, reading from a file, or querying a database), it releases the GIL. This allows another thread to run. Therefore, the threading module is still highly effective for tasks that spend most of their time waiting, as it allows for concurrency.

How to achieve true parallelism?

To bypass the GIL and leverage multiple CPU cores for CPU-bound tasks, you must use the multiprocessing module. It creates separate processes, each with its own Python interpreter and memory space, so the GIL of one process does not affect the others.

tags: #Python #Interview #CodingInterview #GIL #Concurrency #Threading #Multiprocessing #SoftwareEngineering

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By: @DataScienceQ ✨