In the last installment of the ‘Courage to Learn ML’ series, our learner and mentor focus on learning two essential theories of DNN training, gradient descent and backpropagation.
Their journey began with a look at how gradient descent is pivotal in minimizing the loss function. Curious about the complexities of computing gradients in deep neural networks across multiple hidden layers, the learner then turned to backpropagation. By decompose the backpropagation into 3 components, the learner learned about backpropagation and its use of the chain rule to calculate gradients efficiently across these layers. During this Q&A session, the learner questioned the importance of understanding these complex processes in an era of automated advanced deep learning frameworks, such as PyTorch and Tensorflow.
This is the first post of our deep dive into Deep Learning, guided by the interactions between a learner and a mentor. To keep things digestible, I’ve decided to break down my DNN series into more manageable pieces. This way, I can explore each concept thoroughly without overwhelming you.
Today’s discussion promises to address this question by focusing on the challenge of unstable gradients, a major factor making DNN training difficult. We’ll explore various strategies to address this issue, using an analogy of running a miniature ice cream factory, aptly named DNN (short for Delicious Nutritious Nibbles), to illustrate effective solutions. In subsequent posts, the mentor will talk about each solution in detail, showing how these solutions are implemented within the PyTorch framework.
Diving into the world of DNNs, we’re going to use a unique analogy that I’ve been fond of — envisioning DNN as an ice cream factory. Curiously, I once asked ChatGPT what ‘DNN’ might stand for in the realm of ice cream, and after 5 minutes of thinking, it suggested “Delicious Nutritious Nibbles.” I loved it! So, I’ve decided to embrace this playful analogy to help demystify those daunting DNN concepts with a dash of sweetness and fun. As we delve into the depths of deep learning, imagine we’re managers running…