Senseforth Interview Questions, Process, and Tips

One of the challenges in the project was integrating multiple NLP models with different architectures.

• Ensuring compatibility and consistency between models

• Handling different input formats and output structures

• Optimizing performance and computational resources

• Addressing potential conflicts or biases between models

Text embedding is a technique to convert text data into numerical vectors for machine learning models.

• Text embedding captures semantic meaning of words in a continuous vector space.

• Popular methods include Word2Vec, GloVe, and BERT.

• Embeddings can be pre-trained or learned from scratch depending on the task.

• They are used in NLP tasks like sentiment analysis, text classification, and machine translation.

Optimizers exist in FFNs to update weights, but many exist due to different optimization techniques and trade-offs.

• Different optimizers use different optimization techniques such as momentum, adaptive learning rates, and regularization.

• Optimizers have different trade-offs such as convergence speed, generalization, and robustness to noisy data.

• The choice of optimizer depends on the specific problem and data set.

• Examples...read more

Layer normalisation is a technique used to normalise the inputs to each layer of a feedforward neural network.

• It is similar to batch normalisation but normalises the inputs to each layer instead of the entire batch.

• It helps in reducing the internal covariate shift problem.

• It can be applied to any type of activation function.

• It is particularly useful in recurrent neural networks.

• Example: LayerNorm in PyTorch.

Build an airline system to recommend routes from city 1 to city 2 with direct and connecting flights.

• Create a graph with cities as nodes and connections as edges

• Use Dijkstra's algorithm to find shortest path between city 1 and city 2

• For connecting flights, find all possible paths with one or more stops

• Sort and recommend routes based on total distance and number of stops

This function takes a string input and returns all possible combinations of characters in that string without repeated letters.

• Use the itertools module to generate all possible permutations of the string.

• Filter out the permutations that have repeated letters using a set.

• Convert the filtered permutations into a list of strings.