Skip to main content

Understanding Deep Learning through the Art of Baking

·3 mins

Introduction: #

In my path as a software engineer I’ve always been interested in artificial intelligence and the seemingly unlimited potential it possessed. One of my first projects used NLP to analyze job postings to help job hunters create better resumes and even completed the Google Data Analytics to further dive into the career. But I put my pursuit on hold as I saw to go into the field it required PhD’s and higher education. Thankfully recently I found that dismisses that idea and helps people dive right in, my favorite way of learning, and learn how to become a practicioner.

I’m excited to be on this journey further into the world of AI, and per advice from the co-founder Jeremey Howard, witll write about thing that I would have found interesting 6 months ago. Today, I’ll start with the basics and outline the process of training a deep learning model, and to make it easier, compare it to something we all know and love, baking a cake.

A cake with layers representing the architecture of a deep learning model

Step 1: Ingredients and Parameters #

We can fist think about our ingredients that our cake needs, there is set of items and a quantity attached to each. Similarly our ingredients in DL are the inputs or data points that we feed the model.

While parameters are like the specified amounts of each ingredient and the oven temperature. In a nueral network, parameters are the weights an dbiases that the model adjests during the learning process to imporve its predictions.

Step 2: The Recipe (Architecture) #

The architecture of a deep learning model is like our cake recipe. It tells us how to mix the ingredients and in what order. In a neural network, the architecture includes the arrangement of layers, types of layers (like dense, convolutional, or recurrent), activation functions, and the connections between neurons.

Step 3: Baking the Cake (Predictions) #

Predictions are the cakes we create by following our recipe (architecture) with the given inputs and parameters. In deep learning, predictions are the outputs of the model based on the inputs and current parameters (weights and biases).

Step 4: The Ideal Cake (Labels) #

Labels are the perfect cakes we’re aiming for. They represent the ideal outcome that we want to achieve. In deep learning, labels are the actual or correct outputs that we compare our predictions to during the training process.

Step 5: Evaluating Our Cake (Loss) #

The loss is the difference between the cake we made (predictions) and the ideal cake (labels). In deep learning, the loss function measures the difference between our predictions and the actual outputs or labels. The goal is to minimize this loss, which means making our predictions as close as possible to the labels.

Step 6: Improving Our Recipe (Updating Parameters) #

After evaluating the loss, we need to adjust the parameters (ingredient amounts and oven temperature) to make a cake that’s closer to the ideal one. In deep learning, we use optimization algorithms, like gradient descent, to update the model’s weights and biases, which are the parameters of the neural network.

Conclusion: #

There you have it – an easy-to-understand introduction to deep learning using the delightful example of baking a cake! I hope this analogy cake helps you understand some of the complex concepts involved in deep learning. Stay tuned for more insights from my adventure, Happy learning and happy baking!