Deep Learning CakeDeep Learning in Simple StepsWelcomeclass!Mrs. Parker Welcome class! Today, we’re going to bake a cake, but this cake is a metaphor for understanding deep learning.Youhavetobakeacakeinsteps.Mrs. Parker You have to bake a cake in steps. First, you have to bake the actual cake. Think of it as K-12 schooling. We expose a machine to tons of text data, adjusting its 'knobs' to understand language.Butwhatcomesnextafterthecakeisbaked?Timmy But what comes next after the cake is baked?Thenyouaddthefrosting,justlikeinmachinelearningwherewedosupervisedfine-tuning.Mrs. Parker Then you add the frosting, just like in machine learning where we do supervised fine-tuning. It's like going to university, where it learns specific tasks.Sothefrostingisliketeachingthemachinebeachatbot?Timmy So the frosting is like teaching the machine be a chatbot?Exactly!Mrs. Parker Exactly! Finally, you put a cherry on top, which represents reinforcement learning. It's like job onboarding, where it gets feedback to improve.So,Timmy So, the cherry is like feedback that helps the machine think?Yes!Mrs. Parker Yes! It generates responses, and based on feedback, it learns to refine its skills.Gotit.Timmy Got it. It’s all about layers! First the foundation, then the specifics, and finally the refinement.Plot: A tracher explains how deep learning works Quotes: You have to bake a cake in steps. First you have to bake the cake. Then you have to put the frosting and then you can put the cherry on top. Machine learning is very similar. First you have to do large scale at pretraining, then do supervised fine tuning then do Reinforcement learning on top. Pretraining is K-12: Imagine a machine packed with adjustable knobs. You expose it to vast swathes of text data, tweaking the knobs endlessly so it gets insanely good at predicting the next word. This process builds a massive, general understanding of language and world knowledge – the essential foundation. Supervised Fine-Tuning is University: Now, you teach it a specific 'major' – how to be a helpful chatbot. You feed it high-quality Q&A examples, tuning it specifically to respond usefully and safely. It learns the application of its knowledge. Reinforcement Learning is Job Onboarding: Finally, you put it to work. It generates answers, humans rate them ('Good job!' vs. 'Try again'), and the model learns from this real-world feedback loop. This refines its conversational nuances and practical helpfulness. Objects: Oven, Mixing bowl, Whisk, Cake pan, Ingredients (flour, sugar, eggs, etc.), Frosting, Spatula, Cherry, Plate, Knife @Mrs. Parker: Unpredictable Examiner @Timmy: Resourceful 8yr old +Theme: Rendered in the style of abstract urban portraiture, featuring dynamic brushstrokes and vibrant splashes of color. The artwork combines elements of street art and digital painting, integrating geometric shapes and vivid contrasts to create a sense of movement and energy. The style is characterized by its bold use of color and texture, blending realism with abstract expressionism to produce a striking visual impact.
