Artificial Intelligence course in chennai

Supervised learning involves training models on labeled datasets, where both the inputs and their corresponding outputs are provided. For example, image classification where images are labeled as cats or dogs. Unsupervised learning deals with unlabeled data and tries to find hidden structures, like clustering customers based on purchase patterns. Reinforcement learning trains an agent to make sequences of decisions by rewarding desired outcomes, like training a robot to navigate a maze using trial and error.

Activation functions add non-linearity to neural networks, allowing them to model and learn complex relationships within the data. Without non-linear activation functions, no matter how many layers the network has, the output would be a linear function of the input, limiting the model’s power to capture real-world data relationships.

Batch gradient descent computes gradients using the entire dataset, which can be slow but provides stable convergence. Stochastic gradient descent (SGD) updates weights using one data point at a time, making it faster and allowing the model to escape local minima but with more noise in the updates. Mini-batch gradient descent strikes a balance by using small subsets of the data, leading to faster and more stable training compared to using the entire dataset or just one sample at a time. I prefer SGD or mini-batch for large datasets.

A confusion matrix summarizes a classification model’s performance by showing true positives, true negatives, false positives, and false negatives. It helps evaluate accuracy, precision, recall, and F1-score, which give a more complete picture of model performance, especially when dealing with imbalanced datasets.

Dropout randomly disables a fraction of neurons during training, preventing the co-adaptation of features. This forces the network to learn more robust representations and reduces overfitting by making the model less reliant on specific neurons.

I start with text preprocessing—tokenization, lowercasing, removing stop words, and stemming/lemmatization. Then, I convert text into numerical form using methods like TF-IDF or word embeddings (e.g., Word2Vec). Finally, I feed the processed data into models like logistic regression, LSTM, or transformers for tasks like sentiment analysis or text classification.

The vanishing gradient problem arises in deep neural networks when gradients shrink significantly during backpropagation, which hinders the model's ability to learn long-term dependencies. LSTM networks address this by using gates that control information flow, allowing gradients to pass unchanged for longer sequences, which improves learning in sequential data.

Bias is the error introduced when a model makes strong assumptions that oversimplify the problem, typically causing underfitting. Variance, conversely, occurs when a model is too sensitive to small changes in the training data, which can lead to overfitting. Balancing involves selecting a model complex enough to capture patterns but simple enough to generalize well, often using techniques like cross-validation and regularization.

CNNs use convolutional layers to automatically extract spatial features from images by applying filters that detect edges, textures, and shapes. Pooling layers reduce spatial dimensions, making computation efficient. Their ability to capture local patterns and hierarchical features makes them highly effective for tasks like image classification and object detection.