Satyam Mishra

Atir Mishra, I am a data scientist. I worked with Radcliffe Labs where I worked on building a recommendation system. And at Amaruzala, I also worked on a recommendation system, and I developed the recommendation system that caters to millions of users. At Radcliffe Labs, I designed a computer vision system for fluoro grooming detection. This is a brief introduction about myself. I did a BCA from IIGN and have certifications from IIT Madras in machine learning and TensorFlow, and also from Datachem as a power data scientist with Python skills.

Which I think has to be most efficient for indexing when working on machine learning problems.

What approach would you take to handle imbalanced classes in a dataset when training a deep learning model using PyTorch? You can basically apply techniques like ensemble methods, resampling techniques, data augmentation techniques, and weighted losses. PyTorch has inbuilt functionality of cross entropy loss, which is like a weighted loss function. It also has some other functionalities like precision recall and F1 score to evaluate.

One of the best use cases I found for Python's lambda function in preprocessing data for a machine learning model is that I can just build a function, sorted from a version of a function that's required for cleaning up and starting with specific data. Like, I wanted to do some scaling, or maybe I can also do some other things like normalization, or maybe even feature extraction, which can be done with a single line of code using a lambda function.

Regularization techniques in Python's scikit-learn library, such as L1 and L2 regularization, help prevent overfitting during model training.

I would use Recursive feature elimination or Lasso regression for feature selection in a large-scale machine learning project.

numeric_vector <- c(1, 2, 3, 4, 5) for (i in 1:length(numeric_vector)) { sum_value <- 0 for (j in 1:i) { sum_value <- sum_value + numeric_vector[j] } mean_value <- sum_value / i print(mean_value) } Corrected Code: numeric_vector <- c(1, 2, 3, 4, 5) sum_value <- 0 for (i in 1:length(numeric_vector)) { for (j in 1:i) { sum_value <- sum_value + numeric_vector[j] } mean_value <- sum_value / i print(mean_value) }

in the neural network, training loop written in the Python, identify the section of code. in neural network training loop written in that Python, identify that section of the loop code that might cause an error during back propagation. What is the and that import while touching what quite might cause in that provision and explain why. so it really is what you need to do. This might be problematic. Mod.eval. Okay. so how I should I do that? And then not relu. and are not linear. I've crossed 2, long dot tensor. The line target dot on this typo, it should be long tensor 1. That's

Which when hyper tuned are parameters for PyTorch model, which method do you prefer? Good search or random search? Hyperparameter tuning. I would use grid search.

When developing a PyTorch model, how do you ensure efficient memory usage during training, especially with a large dataset? Okay. When developing a pilot class model, how do you ensure efficient memory usage during training, especially with larger datasets. So, in that case, when developing a PyTorch model to ensure efficient memory usage, in PyTorch, I would consider using techniques like batch loading, data augmentation, optimizing your model architecture for memory. Additionally, monitoring GPU memory usage and clearing unnecessary variables can also help with memory effectively.