You have $0 for marketing Your product just launched. How will you get users?

can u guess what language is this? 😎

fable 5 be like: lemme alone 🥺

Instead of thinking how to do it. Do it

😂

Are you making money with computer science?

AI meeting be like 😂: Claude: We should carefully consider the ethical implications Codex: I already built it, deployed it, and wrote 300 tests DeepSeek: I did it 10x cheaper Gemini: I found 500 documents about it ChatGPT: What was the original problem again? Grok: What if we made it weirder?

This 😂

don't break this rule 😂

why is so hard 😅

😂

This is a free world don't limit yourself, be happy

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TOP AI ENGINEERING TERMS 🏆 Data: raw information used to learn or decide. Example: user clicks, text, images, numbers. Dataset: organized collection of data. Example: a CSV file of 10,000 user transactions. Model: a system that learns patterns from data. Example: a spam detection system. Algorithm: a step-by-step method to solve a problem. Example: decision tree algorithm. Training: teaching a model using data. Example: feeding emails to learn spam vs not spam. Inference: using a trained model to make predictions. Example: checking a new email if it is spam. Parameters: learned knowledge inside a model. Example: weights inside a neural network. Features: important inputs used for learning. Example: email length, sender, keywords. Labels: correct answers used for training. Example: “spam” or “not spam”. Loss: how wrong the model is. Example: 20% error on predictions. Optimization: making the model better step by step. Example: adjusting weights to reduce error. Overfitting: memorizing instead of learning patterns. Example: perfect on training data but bad on new data. Underfitting: not learning enough from data. Example: model fails on both training and test data. Generalization: working well on new unseen data. Example: correctly classifying new emails. Bias: systematic error in predictions. Example: always predicting lower house prices. Variance: sensitivity to small data changes. Example: model changes a lot with small dataset changes. Hyperparameters: settings chosen before training. Example: learning rate = 0.01. Neural Network: layered system that learns patterns. Example: image recognition model. Weights: importance values inside a model. Example: how strongly a feature affects prediction. Embedding: converting data into numerical meaning space. Example: word “king” becomes a vector. Token: small piece of text for AI. Example: “cat” or part of a sentence. Context Window: how much text AI can remember. Example: 8,000 tokens of conversation history. Batch: small chunk of data used for training. Example: 32 images at a time. Epoch: one full pass through dataset. Example: reading all training data once. --- Generative AI with Python Grab your copy now: hernandoabella.com

Remember to work on only one thing at the same time 😂

Do not over engineering

What is your biggest dream now?

A cup of coffee is what you need to be very productive

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