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NX Block Trades Pvt. Ltd.
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I applied via Approached by Company and was interviewed in Sep 2023. There were 3 interview rounds.
Reinforcement Learning is a type of machine learning where an agent learns to make decisions by interacting with an environment.
Reinforcement Learning involves an agent learning to take actions in an environment to maximize some notion of cumulative reward.
It uses a trial and error approach, where the agent learns from the consequences of its actions.
In Automated Trading Systems, Reinforcement Learning can be used to o...
Sentiment Analysis of News
RNNs are a type of neural network designed for sequence data. LSTM is a type of RNN that can learn long-term dependencies.
RNNs are designed to work with sequential data, such as time series or text data.
LSTM (Long Short-Term Memory) is a type of RNN that addresses the vanishing gradient problem by introducing a memory cell.
LSTM has three gates - input gate, forget gate, and output gate - that control the flow of inform...
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Overfitting occurs when a machine learning model learns the training data too well, including noise and outliers, leading to poor generalization on new data.
Overfitting happens when a model is too complex and captures noise in the training data.
It leads to poor performance on unseen data as the model fails to generalize well.
Techniques to prevent overfitting include cross-validation, regularization, and early stopping.
...
Overfitting occurs when a model learns the details and noise in the training data to the extent that it negatively impacts the model's performance on new data.
Overfitting happens when a model is too complex and captures noise in the training data.
It leads to poor generalization and high accuracy on training data but low accuracy on new data.
Techniques to prevent overfitting include cross-validation, regularization, and...
Overfitting occurs when a machine learning model learns the training data too well, including noise and outliers, leading to poor generalization on new data.
Overfitting happens when a model is too complex and captures noise in the training data.
It leads to poor performance on unseen data as the model fails to generalize well.
Techniques to prevent overfitting include cross-validation, regularization, and early stopping.
...
Overfitting occurs when a model learns the details and noise in the training data to the extent that it negatively impacts the model's performance on new data.
Overfitting happens when a model is too complex and captures noise in the training data.
It leads to poor generalization and high accuracy on training data but low accuracy on new data.
Techniques to prevent overfitting include cross-validation, regularization, and...
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