Untitled Flashcards Set

1. Training Set

• The training set is the collection of data we use to teach a machine learning model.

• It includes multiple examples (also called data points or instances).

• The model learns patterns from this data by adjusting its internal parameters.

• Example:

  • If we are predicting house prices, our training set might include a table like this:

Here, the first two columns are features, and the third column is the label (what we want to predict).

2. Example (Instance, Data Point, Sample)

• An example is one row from the training set.

• Each example has features (inputs) and a label (output).

• Example:

  • (1200, 10) → 250,000 → One house's size and age correspond to a predicted price.

3. Label (Target, Output)

• The label is the value we are trying to predict.

• It is also called the dependent variable in statistics.

• Example:

  • In house price prediction, the price is the label.

  • In spam detection, the label is "Spam" or "Not Spam".

4. Features (Inputs, Covariates)

• Features are the input variables used to make predictions.

• They are also called independent variables.

• Example:

  • If predicting house prices, the features could be:

    • Size of the house (sq ft)

    • Age of the house (years)

    • Number of bedrooms

5. Linear Regression

• Linear regression is a simple machine learning model that assumes the relationship between features and labels is a straight line.

• Formula:

  Prediction=(Feature×w)+b\text{Prediction} = (\text{Feature} \times w) + bPrediction=(Feature×w)+b

  where:

  • www = weight (how much influence a feature has).

  • bbb = bias (a constant adjustment).

• Example:

  • Predicting house price:

    • Price=(Size×200)+50,000\text{Price} = (\text{Size} \times 200) + 50,000Price=(Size×200)+50,000

6. Weights & Bias

• Weights (w) → Numbers that determine how important each feature is.

• Bias (b) → A constant that adjusts the predictions when all features are zero.

• Example:

  • If Size matters more than Age, the model might learn:

    • Price=(Size×200)+(Age×−500)+50,000\text{Price} = (\text{Size} \times 200) + (\text{Age} \times -500) + 50,000Price=(Size×200)+(Age×−500)+50,000

    • The negative weight for Age means older houses tend to be cheaper.

7. Loss Function

• The loss function measures how wrong the model's predictions are.

• It gives a number that tells us how far our predictions are from the actual values.

• A common loss function is Mean Squared Error (MSE):

  Loss=1n∑(Actual Value−Predicted Value)2\text{Loss} = \frac{1}{n} \sum (\text{Actual Value} - \text{Predicted Value})^2Loss=n1​∑(Actual Value−Predicted Value)2

• The goal is to make this loss as small as possible.

8. Gradient Descent

• Gradient Descent is how a model learns by adjusting weights & bias step by step.

• Imagine walking downhill toward the lowest point of a valley.

• The slope tells us which direction to move to reduce loss.

• Learning too fast (big steps) → Might skip the lowest point.

• Learning too slow (small steps) → Takes forever.

9. Stochastic Gradient Descent (SGD)

• Instead of calculating the gradient from all data, SGD updates the weights using just one example at a time.

• Faster but noisier than full gradient descent.

• Used when we have huge datasets.

10. Minibatch Gradient Descent

• A compromise between batch and stochastic gradient descent.

• Instead of updating after one example (SGD) or all examples (batch), minibatch takes a small group (e.g., 32 examples) at a time.

• Balances speed and stability.

11. Hyperparameters vs. Parameters

Parameters (Learned Automatically)

• These are values the model learns during training.

• Example: Weights and bias in linear regression.

Hyperparameters (Manually Set by Humans)

• These control how the model learns but are not learned automatically.

• Examples of hyperparameters:

  • Learning Rate (step size in gradient descent).

  • Batch Size (number of examples in minibatch gradient descent).

  • Number of Epochs (how many times we go through the dataset).

• Hyperparameters are tuned by trial and error.

12. Validation Set

• A separate dataset used to check if the model is working well on new data.

• Helps prevent overfitting (when a model memorizes instead of generalizing).

• Not used for training, just for testing.

13. Vectorization

• Instead of looping over data one by one, we use matrices and vectors to speed up calculations.

• Modern ML libraries use matrix operations (NumPy, PyTorch, TensorFlow) for faster training.

• Example:

  • Instead of adding numbers one by one, we add two lists in one operation:

    python

    CopyEdit

    a = np.array([1,2,3]) b = np.array([4,5,6]) c = a + b # Faster than looping

14. Biological Neurons & Artificial Neurons

ML models were inspired by real neurons in the brain.

Neural Networks mimic how real neurons fire signals to learn patterns.

Final Takeaways

• Machine learning is about learning from data.

• Linear regression finds a straight-line relationship between inputs and outputs.

• Weights and bias control how predictions are made.

• Loss functions measure errors, and gradient descent helps improve the model.

• Hyperparameters are set manually, while parameters are learned.

• Validation sets prevent overfitting.

• Vectorization speeds up ML computations.

• Neural networks are inspired by the brain but work mathematically.

Now you should be completely prepped for your quiz! 💪🔥 Let me know if anything still feels unclear! 🚀

You said:

Instead of looping over data *one by one**, we use matrices and vectors to speed up calculations. Modern ML libraries use *matrix operations (NumPy, PyTorch, TensorFlow)** for faster training.

i need you to redo this and explain weights and biases better

ChatGPT said:

Alright, let’s go deeper into weights and bias and explain them more clearly.

Weights & Bias Explained in Simple Terms

What Are Weights?

• Weights control how much influence each feature has on the prediction.

• Think of weights like knobs on a stereo—adjusting them changes the output sound (prediction).

• A large weight → That feature is very important for prediction.

• A small or zero weight → That feature doesn't matter much.

Example: Predicting House Price

If we predict house price using size and number of bedrooms, our formula looks like:

Price=(Size×w1)+(Bedrooms×w2)+b\text{Price} = (\text{Size} \times w_1) + (\text{Bedrooms} \times w_2) + bPrice=(Size×w1​)+(Bedrooms×w2​)+b

where:

• w1w_1w1​ and w2w_2w2​ are weights.

• bbb is the bias (we'll get to that next).

Let’s say the model learns:

• w1=300w_1 = 300w1​=300 → Every extra square foot increases price by $300.

• w2=10,000w_2 = 10,000w2​=10,000 → Each extra bedroom increases price by $10,000.

If a house has 1000 sq ft and 3 bedrooms, then:

Price=(1000×300)+(3×10,000)+b\text{Price} = (1000 \times 300) + (3 \times 10,000) + bPrice=(1000×300)+(3×10,000)+b

What is Bias?

• Bias is a constant that shifts the predictions up or down.

• It’s like setting a default base price before considering other factors.

• Even if all features are 0, bias ensures the model can still output a value.

Example:

• If bias b=50,000b = 50,000b=50,000, the model’s prediction starts at $50,000 before features are considered.

• Without bias, the model would always predict 0 when all inputs are 0.

Key Difference: Weights vs. Bias

Analogy: Making a Sandwich 🥪

• Weights = The ingredients (meat, cheese, lettuce).

  • Adding more cheese makes the sandwich cheesier (like how weights increase feature importance).

• Bias = The plate under the sandwich.

  • Even without ingredients, there’s still something there (like bias ensures a base prediction).

Why Do We Need Both?

• If we only had weights, our model would always predict 0 when inputs are 0.

• Bias ensures that even with no features, the model can still make reasonable predictions.

Final Takeaways

✅ Weights = Feature importance (adjusted during training)
✅ Bias = Base prediction shift (ensures outputs aren’t stuck at 0)
✅ Both are learned by the model using gradient descent to improve accuracy.

Now, this should be super clear! 🚀 Let me know if anything needs more detail!

You said:

wait what the difference betwene hyperparameters and parameters don't make sens

ChatGPT said:

Great question! Let’s break it down clearly so you understand the difference between parameters and hyperparameters.

1. What Are Parameters?

• Parameters are the values that the model learns automatically during training.

• These include weights and biases in a model.

• They are updated by the model using optimization techniques like gradient descent.

Example: House Price Prediction

If our model is:

Price=(Size×w1)+(Bedrooms×w2)+b\text{Price} = (\text{Size} \times w_1) + (\text{Bedrooms} \times w_2) + bPrice=(Size×w1​)+(Bedrooms×w2​)+b

• w1w_1w1​ and w2w_2w2​ (weights) and bbb (bias) are parameters.

• The model learns these values by minimizing loss.

2. What Are Hyperparameters?

• Hyperparameters are settings that humans manually choose before training begins.

• They are not learned by the model.

• They control how the model learns but are not changed during training.

Examples of Hyperparameters

1. Learning Rate (α) → Controls how fast the model updates weights.

   • If too high, the model might overshoot the best solution.

   • If too low, the model learns too slowly.

2. Batch Size → Number of examples the model processes before updating parameters.

   • Large batch = More stable but slower updates.

   • Small batch = Faster updates but noisier.

3. Number of Epochs → How many times the model sees the entire dataset.

4. Number of Hidden Layers (Neural Networks) → Controls model complexity.

3. Key Differences

4. Analogy: Cooking a Dish 🍲

• Parameters = Ingredients the chef adjusts during cooking (like adding more salt to balance the taste).

• Hyperparameters = Oven temperature and cooking time (set before cooking starts).

👉 If the temperature is too high (bad hyperparameter), the dish burns. If it’s right, the dish cooks perfectly!

5. Why Does This Matter?

• Choosing bad hyperparameters → The model may not learn properly.

• Tuning hyperparameters correctly → Helps the model learn faster and better.

Hyperparameters are usually found using trial and error or automated techniques (like Grid Search, Bayesian Optimization).

Final Takeaways

✅ Parameters = Weights & Bias, learned by the model.
✅ Hyperparameters = Learning rate, batch size, etc., set manually before training.
✅ Model updates parameters, but hyperparameters stay fixed unless manually tuned.

Now, it should be crystal clear! 🚀 Let me know if you need another example! 😃

4o