Hands on experience: How AI learn?

Let's explain how AI learns using a simple example of teaching a computer to recognize handwritten digits, like those found in a phone number or a zip code. This task is known as digit recognition and is a fundamental problem in machine learning.

Here's a simplified explanation:

1. Data Collection: First, we gather a dataset of handwritten digits along with their corresponding labels (i.e., the actual digit each image represents). This dataset serves as our training data.

2. Data Preprocessing: Next, we preprocess the data by converting the images into a format that the computer can understand. This often involves resizing the images and converting them into numerical values (pixels).

3. Model Selection: We choose a machine learning model to train on our data. For digit recognition, a popular choice is a type of model called a neural network.

4. Training the Model: We feed our preprocessed data into the neural network and let it learn the patterns in the data. During training, the neural network adjusts its internal parameters (weights and biases) to minimize the difference between its predictions and the actual labels in the training data.

5. Evaluation: After training, we evaluate the performance of our model using a separate dataset called the validation set. This set contains examples that the model hasn't seen during training. We measure metrics like accuracy to assess how well the model generalizes to new, unseen data.

6. Testing: Finally, we test our trained model on another separate dataset called the test set to see how well it performs on completely new examples.

Here's a simple Python code example using the popular machine learning library called TensorFlow to train a neural network for digit recognition:

import tensorflow as tf

# Load the MNIST dataset (a dataset of handwritten digits)

mnist = tf.keras.datasets.mnist

(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

# Preprocess the data

train_images = train_images / 255.0

test_images = test_images / 255.0

# Define the model architecture

model = tf.keras.Sequential([

tf.keras.layers.Flatten(input_shape=(28, 28)),

tf.keras.layers.Dense(128, activation='relu'),

tf.keras.layers.Dense(10)

])

# Compile the model

model.compile(optimizer='adam',

loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),

metrics=['accuracy'])

# Train the model

model.fit(train_images, train_labels, epochs=5)

# Evaluate the model

test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)

print('\nTest accuracy:', test_acc)

In this code:

1. We load the MNIST dataset (MNIST Dataset (kaggle.com)), which contains 60,000 training images and 10,000 test images of handwritten digits.
2. We preprocess the data by scaling the pixel values to be between 0 and 1.
3. We define a simple neural network model with one input layer, one hidden layer with 128 neurons, and one output layer.
4. We compile the model with appropriate loss function and optimizer.
5. We train the model on the training data for 5 epochs.
6. We evaluate the model's performance on the test data and print the test accuracy.

Sorry I am not able to understand Point 3!!

Let's break down the terms "one input layer, one hidden layer with 128 neurons, and one output layer" in simpler language using an analogy with a classroom scenario:

Imagine you're in a classroom (which represents our neural network), and you have three different types of students (layers) in the classroom: input students, hidden students, and output students.

1. Input Students: These are the students who receive information or input from the teacher. In our example, let's say each student represents one pixel in the image of a handwritten digit. So, if we have a 28x28 pixel image (like the ones in our digit dataset), we'd have 784 (28x28) input students. Each student passes on the information they receive to the next group of students.

2. Hidden Students: These are the students who process the information they receive from the input students. In our classroom, we have one group of hidden students, and there are 128 of them. Each hidden student can be thought of as having a different specialty or interest. For example, one hidden student might be really good at recognizing straight lines, while another might be good at recognizing curves. These hidden students work together to understand the handwriting in the image.

3. Output Students: These are the students who provide the final answer or output based on the information they've received and processed. In our classroom, we have 10 output students because we want to recognize digits from 0 to 9. Each output student represents one possible digit. After receiving information from the hidden students, they decide which digit they think the handwriting represents. For example, if the output student representing the digit "7" is most confident based on the information it received, it will raise its hand to indicate that it thinks the digit is a "7".

So, in our classroom (neural network) scenario, we have input students who receive information (pixels from the image), hidden students who process that information (128 students with different specialties), and output students who give the final answer (10 students representing digits 0-9). Together, they work as a team to recognize handwritten digits!

Please also explain point 4 in detail!!

Let's break it down:

Imagine you're learning to play a new video game. As you play, you want to improve your skills and get better scores. To help you learn faster, you decide to keep track of your performance after each game.

Now, think of the "loss function" like a scorekeeper. Its job is to tell you how well you're doing compared to how well you should be doing. For example, if your goal is to score 100 points in the game, but you only score 80 points, the loss function would tell you that you're "losing" 20 points.

In our example of training a computer to recognize handwritten digits, the loss function plays a similar role. It measures how far off the predictions of the computer are from the actual labels of the digits in our training data. The goal of training the computer is to minimize this loss, just like your goal in the game is to maximize your score.

Now, let's talk about the "optimizer." Imagine you have a coach who helps you improve your skills in the video game. Your coach watches you play, identifies areas where you could do better, and gives you advice on how to improve.

In machine learning, the optimizer is like your coach. It's a tool that helps the computer adjust its internal parameters (weights and biases) to minimize the loss function. It watches how well the computer is doing on the training data, identifies ways it could improve, and updates its parameters accordingly.

In our example code, when we say "we compile the model with an appropriate loss function and optimizer," we're essentially setting up the rules of the game for the computer. We're telling it how to keep track of its performance (the loss function) and how to improve over time (the optimizer).

So, in simpler terms, compiling the model with an appropriate loss function and optimizer is like setting up the game for the computer to play. It tells the computer how to measure its performance and how to get better at recognizing handwritten digits as it learns from the training data.

This is a simplified example, but it illustrates the basic steps involved in teaching a computer to learn from data using machine learning techniques.