Yes, inductive biases are indeed necessary in neural networks and machine learning models in general to improve their learning effectiveness. An "inductive bias" in machine learning refers to a set of assumptions that a learning algorithm uses to predict outputs given inputs it has not encountered, and it guides the learning algorithm by making some hypotheses more likely than others. Without an inductive bias, a model has no preferences and could make highly unreasonable predictions, thereby reducing its performance. Here's why inductive biases are necessary: 1. **Prevents Overfitting:** Biases can help to prevent overfitting by simplifying the model. This reduces the model’s problem-solving capacity to avoid fitting to noise and, instead, capture more significant patterns in the data. 2. **Solves Under-determined Problems:** Many learning problems are under-determined, where the number of possible hypotheses that explain the data accurately is infinite. In such cases, inductive biases help chooses between these equally good solutions. 3. **Reduces Learning Time:** By providing prior knowledge about which types of solutions should be searched, biases can speed up the learning time. Here are some relevant sources that thoroughly cover the topic of inductive bias: 1. Mitchell, T. M. (1980). “The Need for Biases in Learning Generalizations”. Department of Computer Science, Laboratory for Computer Science Research. 2. Geman, Stuart, Elie Bienenstock, and René Doursat. (1992). "Neural networks and the bias/variance dilemma." Neural computation 4.1: 1-58. 3. Haussler, David. (1988) "Quantifying inductive bias: AI learning algorithms and Valiant's learning framework." Artificial Intelligence 36.2: 177-221. By looking into these sources, you will get detailed insights into the necessity and advantages of incorporating inductive biases in neural networks and general machine learning models.