Brewing Neural Networks with TensorFlow: A Coffee Example for Beginners

Machine learning can feel intimidating if you’re starting from zero. But let’s make it fun: imagine you’re a barista predicting what coffee a customer wants. We’ll use TensorFlow to build a simple neural network that learns these patterns.

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🛠 What is TensorFlow?

TensorFlow is an open‑source library created by Google. Think of it as a toolbox that helps us build and train neural networks. Instead of writing rules manually, we give TensorFlow examples, and it figures out the rules itself.

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🧠 What is a Neural Network?

A neural network is inspired by how our brain works. It has:

• Inputs → information we feed in (like sleepiness, time of day, stress level).
• Hidden layers → where the “thinking” happens.
• Outputs → the prediction (espresso, latte, or black coffee).

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☕ The Coffee Example

We’ll predict coffee choice based on multiple inputs:

1. Sleepiness level (0–10)
2. Time of day (0–10)
3. Stress level (0–10)
4. Weather (0 = cold, 1 = hot)

Outputs:

• Espresso = 0
• Latte = 1
• Black Coffee = 2

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Step 1: Install TensorFlow

pip install tensorflow

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Step 2: Import Libraries

import tensorflow as tf
from tensorflow import keras
import numpy as np

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Step 3: Prepare Data

# Inputs: [sleepiness, time_of_day, stress, weather]
X = np.array([
    [9, 2, 7, 0],   # sleepy, morning, stressed, cold → espresso
    [3, 8, 2, 1],   # relaxed, night, low stress, hot → latte
    [6, 5, 5, 0],   # medium sleepy, afternoon, medium stress, cold → black coffee
])

# Outputs: espresso=0, latte=1, black=2
y = np.array([0, 1, 2])

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Step 4: Normalizing Data

Neural networks work best when inputs are scaled to a similar range. For example, sleepiness (0–10) and weather (0/1) are very different scales. We normalize values between 0 and 1:

X = X / np.max(X, axis=0)

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Step 5: Build the Neural Network

model = keras.Sequential([
    keras.layers.Dense(8, activation='relu'),   # hidden layer
    keras.layers.Dense(8, activation='relu'),   # another hidden layer
    keras.layers.Dense(3, activation='softmax') # output layer
])

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Step 6: Compile the Model

model.compile(
    optimizer='adam',
    loss='sparse_categorical_crossentropy',
    metrics=['accuracy']
)

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Step 7: Train the Model

model.fit(X, y, epochs=100, batch_size=2)

What happens during training?

• The model starts with random weights and biases.
  • Weights are numbers that decide how strongly each input affects a neuron.
  • Biases shift the output up or down.
• During each epoch, TensorFlow adjusts these weights and biases to reduce errors.
• Over time, the network learns the right “recipe” for predicting coffee choices.

You can even inspect them:

for layer in model.layers:
    weights, biases = layer.get_weights()
    print("Weights:", weights)
    print("Biases:", biases)

This shows the actual numbers the network has learned.

What are epochs?

• An epoch = one full pass through the training data.
• If you have 100 samples and train for 10 epochs, the model sees all 100 samples 10 times.

What are batches?

• Instead of feeding all data at once, we split it into batches.
• Example: batch size = 2 → the model sees 2 samples at a time before updating weights.
• This makes training faster and more memory‑efficient.

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Step 8: Test Predictions

test = np.array([[8, 1, 6, 0]]) / np.max(X, axis=0)  # normalize test input
prediction = model.predict(test)
coffee_type = np.argmax(prediction)

coffee_names = ["Espresso", "Latte", "Black Coffee"]
print("Suggested coffee:", coffee_names[coffee_type])

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🔍 Converting Probabilities to Decisions

The model outputs probabilities, e.g.:

prediction = [[0.7, 0.2, 0.1]]

• Espresso: 70%
• Latte: 20%
• Black Coffee: 10%

We use:

np.argmax(prediction)

to pick the index of the highest probability → Espresso.

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📊 Text‑Based Diagram

Inputs: [Sleepiness, Time of Day, Stress, Weather]
        ↓
   [Hidden Layer 1: 8 neurons]
        ↓
   [Hidden Layer 2: 8 neurons]
        ↓
Outputs: [Espresso, Latte, Black Coffee]

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📝 Viewing the Model Architecture

TensorFlow can print the model’s structure with:

model.summary()

Example output:

Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #
=================================================================
 dense (Dense)               (None, 8)                 40
 dense_1 (Dense)             (None, 8)                 72
 dense_2 (Dense)             (None, 3)                 27
=================================================================
Total params: 139
Trainable params: 139
Non-trainable params: 0
_________________________________________________________________

This shows each layer, its size, and how many parameters (weights + biases) it has.

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🎯 Wrapping Up

You just built your first neural network with TensorFlow!

• Inputs = customer mood, time, stress, weather
• Hidden layers = brain thinking
• Output = coffee choice
• Normalization = scaling inputs for better learning
• Epochs & batches = how training is structured
• Weights & biases = what the model learns
• model.summary() = quick view of architecture

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🚀 Next Steps

• Add more inputs (like age, budget, or favorite flavors).
• Try different activation functions (sigmoid, tanh).
• Experiment with optimizers (SGD, RMSprop).
• Collect larger datasets for better accuracy.

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