Reinforcement Learning and Deep RL Python Theory and Projects - DNN Implementation Batch Gradient Descent

The video tutorial explains the implementation of batch gradient descent in neural networks, contrasting it with stochastic gradient descent. It details the necessary code modifications, focusing on loss calculation and updates. The tutorial also discusses the computational resources required for batch processing and highlights the efficiency of vectorized code. Finally, it introduces the concept of mini-batch gradient descent, setting the stage for the next video.

What is the primary difference between stochastic and batch gradient descent?

• Stochastic uses more computational resources than batch.

• Stochastic updates weights after each example, batch updates after all examples.

• Batch updates weights after each example, stochastic updates after all examples.

• Batch is faster than stochastic due to fewer updates.

In batch gradient descent, how is the loss accumulated?

• By averaging the loss after each example.

• By summing the loss over all examples before updating.

• By updating the loss after each example.

• By multiplying the loss by a constant factor.

Why does batch gradient descent require more computational resources?

• It requires large matrix multiplications.

• It updates weights more frequently.

• It processes data sequentially.

• It uses more complex algorithms.

What is a benefit of using vectorized code in batch gradient descent?

• It reduces the need for large datasets.

• It simplifies the code structure.

• It increases computational efficiency by avoiding explicit loops.

• It allows for more frequent weight updates.

How does vectorization improve the speed of batch gradient descent?

• By reducing the number of weight updates

• By simplifying the algorithm

• By using smaller datasets

• By performing operations on large matrices simultaneously

What is the main topic of the next video after batch gradient descent?

• Advanced neural network architectures

• Stochastic gradient descent

• Mini-batch gradient descent

• Hyperparameter tuning

What is a key parameter introduced in mini-batch gradient descent?

• Regularization term

• Mini-batch size

• Momentum

• Learning rate