Epochs, Iterations and Batch Size | Deep Learning Basics

The video provides a comprehensive overview of the general training process for deep learning models, emphasizing the steps involved in making predictions, comparing them with true labels, and updating network weights based on predictions. The speaker explores key concepts such as updating network weights, accumulating gradients, and the distinction between iterations and updates. Additionally, the video delves into training cycles, the significance of the number of epochs for training, and factors influencing the duration of training such as network size and hardware capabilities. It also covers different methods of presenting data to deep learning models, including utilizing the full dataset for accurate training and the challenges associated with batch processing. Lastly, the implications of batch size in stochastic gradient descent, the benefits of mini-batch processing for faster updates, and the noise introduced by this method are discussed.