Ryan McBride

Evaluating Machine Learning Models: A Breakdown Understanding the Problem * Model Evaluation: How do we assess the performance of a machine learning model? * Generalization: We want models that perform well on new, unseen data, not just the training data. * Overfitting: A model that fits the training data too closely can fail

Choosing Activation Functions In the realm of neural networks, selecting the appropriate activation function is crucial for optimal performance. This choice hinges on the specific task at hand. For binary classification problems, where the target label is either 0 or 1, the sigmoid activation function is the go-to choice. This

The evolution of neural networks started from their initial inspiration of the human brain to their current state as powerful machine learning principles. Early Motivations: * Mimicking the Brain: The original goal was to create software that could learn and think like the human brain. * Biological Inspiration: Early neural networks were

Natural Language Processing spans all tasks where the AI gets human language as input. The following are a few examples of such tasks: * automatic summarization, where the AI is given text as input and it produces a summary of the text as output. * information extraction, where the AI is given

How can we bridge the gap between artificial neural networks and the intricate complexities of human cognition, and what insights can we gain about the nature of human intelligence through the study and development of these machine learning models? Neural networks were inspired by the functionalities of the human brain.

Machine learning is changing the way we see the world. Streaming platforms are analyzing our preferences to provide personalized recommendations. Voice recognition like Siri, Google Assistant, and Alexa are improving their natural language processing allowing a more accurate, context-aware voice interactions. Real-time translations are providing a more accurate and contextually