From Zero to Chat-GPT (Part 2)

278

Do you remember how to set up your Replit? [Link to Part 1]

Lesson 1: Python Lists and their Use in Neural Networks

Hey there, future code wizard! Welcome back to our thrilling adventure, where we’re morphing from coding novices into masters of our own ChatGPT. You’ve been amazing so far, and we’re super excited to dive into Part 2 with you. Ready to get started? Let’s go!

In our first chapter here, we’re going to be buddies with a key Python concept – Lists. And not just that, we’ll also be seeing how these lists play a crucial role in the mysterious world of Neural Networks. Sounds exciting? Let’s roll!

Imagine you’ve got a bunch of favorite video games, or ice cream flavors, or maybe, dinosaurs (who doesn’t love a good T-Rex?). How would you keep track of them? That’s right! You’d probably make a list.

Python List

Python lists are just like that. They let you store an ordered collection of items, like a shopping list, or a to-do list. And what’s cooler, your list could contain anything – numbers, words, or even a mix of both!

# List of numbers? Easy!
numbers = [1, 2, 3, 4, 5]

# List of words? You got it!
words = ["hello", "world"]

# A mix? No problem!
mixed = [1, "two", 3, "four"]

And just like a shopping list, you can check items off your Python list one at a time. You do this by accessing them using their index. Remember, in Python, we start counting from 0, not 1. So, the first item is at index 0, the second item is at index 1, and so on.

# Let's check out the first item in our 'numbers' list
print(numbers[0])  # Output: 1

# What about the second item in our 'words' list?
print(words[1])  # Output: "world"

Remember, in Python, we start counting from 0, not 1. So, the first item is at index 0, the second item is at index 1, and so on. But what happens if we try to access an index that doesn’t exist in our list? Let’s try it out:

try:
    print(numbers[5])  # Trying to access the sixth item in a five-item list
except IndexError:
    print("Oops! That index doesn't exist in our list.")

Now, let’s bring in the Neural Networks. Imagine a layer of our network has 3 neurons, and each neuron has 2 inputs. We can use a list of lists to represent the weights for these neurons. Each inner list would be the weights for one neuron. Pretty neat, huh?

weights = [[0.5, 0.6], [0.1, 0.2], [0.3, 0.4]]

List of lists? Let’s take an example. Imagine you’re organizing a sports event. You have different teams, and each team has different players. You can use a list of lists where the larger list represents all teams, and each smaller list represents the players within a team.

Okay, now it’s your turn! Play around with Python lists. Try creating your own list and accessing different elements. Can you make a list of lists to represent the weights and biases of a neural network layer?

Great work getting through Lesson 1! Before we dive into Lesson 2, let’s see if you’ve grasped the concepts from this lesson:

Question 1: What’s the purpose of a list in Python?

Question 2: Can you create a list of your favorite five movies?

Question 3: How would you access the third element in this list?

Once you're confident with these questions, let's move forward.

Fun Practice

  • Make a list of 5 things you can't live without.
  • Can you print the third item on your list?

Fancy a challenge? Create a list of lists, where each inner list contains the weights for a neuron in a neural network. Assume each neuron has 4 inputs. [Try it out in replit!]

Ok, so we saw how Python lists are pretty handy when it comes to organizing stuff. We also saw how they fit into the world of neural networks, by helping us store things like input data and weights. In the next lesson, we're going to explore Python dictionaries and their role in the realm of neural networks.

Lesson 2: Decoding Python Dictionaries and Understanding Flow Control

Hello again, future coding rockstar! Before we dive into Python dictionaries and control flow, let's quickly recall what we learned in our last lesson. We discovered Python lists, learned how we can use them to store and organize data, and even used list of lists to represent neuron weights in a neural network. Now, let's turn our attention to Python dictionaries and control flow.

Dictionaries? Think of a dictionary like a contact list on your phone. The name of the person is the key, and their phone number is the value. If you want to find someone's phone number, you don't have to scroll through all your contacts - you just search for their name. A Python dictionary works in a similar way!

Ever wondered how a real dictionary works? You have a word (the "key"), and that word has a meaning (the "value"). Python dictionaries operate on the same principle. They store key-value pairs.

# Creating a Python dictionary
my_dict = {
  "name": "ChatGPT",
  "type": "AI",
  "age": 3
}

# Accessing values using keys
print(my_dict["name"])  # Output: ChatGPT

Cool, right? Now, what if we want to apply a certain operation to every item in a list or a dictionary? Here's where control flow enters the scene with loops!

Python's for loop allows you to iterate over each item in a list or a dictionary. It's kind of like going through each item on your shopping list to make sure you've got everything.

# Let's iterate over our list of numbers
for number in numbers:
    print(number)
    
# And now over our dictionary
for key in my_dict:
    print(key, ":", my_dict[key])

This is where things get exciting! With what we've learned about lists, dictionaries, and loops, we can start to create a simple model of a neural network layer in Python.

# Let's create a simple representation of a neural network layer
neural_network_layer = {
    "neuron_1": [0.5, 0.6],
    "neuron_2": [0.1, 0.2],
    "neuron_3": [0.3, 0.4]
}

# Now let's print the weights of each neuron
for neuron in neural_network_layer:
    print(neuron, "weights:", neural_network_layer[neuron])

Fancy a challenge? Your mission, if you choose to accept it, is to play around with Python dictionaries and loops. Can you create your own dictionary and print all its keys and values?

And that's a wrap on Lesson 2! We've added dictionaries and control flow to our Python arsenal and seen how they apply to neural networks. Up next, we're going to dive deeper into Python functions and their magical properties.

Lesson 3: Functions – The Building Blocks of AI

Now, we're going to get our hands on something seriously exciting - functions! You may wonder, why are functions important in the world of AI, including our mini chat-GPT? Let's find out.

Functions are a cornerstone of programming. They allow us to group a set of statements together, which can then be executed whenever we want them to. Functions can receive input (parameters), perform an action, and return an output. This mirrors what happens in AI: inputs are processed to give outputs.

Function Diagram

Let's start by defining a simple Python function. We use the def keyword to do this.

def greet(name):
  # The f-string format is a way to embed expressions inside string literals,
  # using curly braces {}. The expressions will be replaced with their values.
  print(f"Hello {name}, nice to meet you!")

f-string? Here we're using print(f"...") formatting to insert the {name} into the string. But we could've used print("Hello,", name, "!") also!

Here, greet is a function that takes one parameter: name. When called with an argument, it prints a greeting for the provided name.

greet("Alice")  # Outputs: Hello, Alice!

In the context of a chatbot, you might have a function that takes user input and responds based on that input:

def respond_to_user(input):
    if input == "Hello":
        return "Hello! How can I assist you today?"
    else:
        return "I'm sorry, I didn't understand that."

In this function, we have an if...else statement, which allows our function to determine what to do based on the user input.

Functions also allow for code reuse. If there's a piece of code that you find yourself writing over and over, that's a good candidate for a function. For example, our mini chat-GPT might need to pre-process user input in the same way each time. Instead of writing the same code every time we want to process the input, we can write a function once and then call that function whenever we need it.

def pre_process(input):
    return input.lower().strip()

This pre_process function takes user input, converts it to lowercase, and removes whitespace from the ends. Now, we can easily pre-process input before responding to it:

def respond_to_user(input):
    input = pre_process(input)
    # The rest of the function...

In the context of a chatbot, you might have a function that takes user input and another parameter, like the bot's mood, and responds based on that input and mood:

def respond_to_user(input, mood):
    if input == "Hello" and mood == "happy":
        return "Hello! How can I assist you today?"
    elif input == "Hello" and mood == "grumpy":
        return "What do you want?"
    else:
        return "I'm sorry, I didn't understand that."

In this function, we have an if…else statement, which allows our function to determine what to do based on the user input and the bot's mood. Functions can take multiple parameters and can return a value, making them a flexible tool in Python.

Remember, functions are about organizing and reusing code. They help us write cleaner, more readable code, and they're a powerful tool in your Python arsenal. Now, let's see how loops can help us in the next lesson.

Lesson 4: The Magic of Loops and Their Role in AI

One of the most powerful concepts in programming is the ability to perform an operation over and over again, quickly and efficiently. This is called looping, and it is central to AI.

For instance, an AI model might need to look at thousands or even millions of data points. Trying to write individual lines of code to handle each data point would be impractical. With loops, we can write one piece of code that can handle as much data as we need it to.

In Python, we have several types of loops, but the two most common ones are for and while loops.

Let's look at a simple for loop:

for i in range(5):
    print(i)

In this example, the loop will run 5 times, and each time it will print the current value of i.

Now, imagine you have a list of user inputs that you want to respond to:

user_inputs = ["Hello", "How are you?", "Tell me a joke"]

for input in user_inputs:
    response = respond_to_user(input)
    print(response)

Here, we're using a for loop to go through each user input, respond to it using our respond_to_user function, and then print the response.

A while loop, on the other hand, continues executing as long as a certain condition is True:

i = 0
while i < 5:
    print(i)
    i += 1

In this example, the loop will keep running and printing the value of i as long as i is less than 5.

In the context of our mini chat-GPT, a while loop could be used to keep the chatbot running until the user decides to stop:

while True:
    user_input = input("You: ")
    if user_input.lower() == "quit":
        break
    response = respond_to_user(user_input)
    print(f"Chatbot: {response}")

In this code, the chatbot will keep asking for user input and responding to it until the user types "quit". The break keyword is used to exit the loop.

These are the basics of loops in Python. Looping is a fundamental concept in programming and especially important in AI. With loops, we can handle large amounts of data, perform repetitive tasks, and create more dynamic and interactive programs, such as a chatbot.

Alright! You've now learned about variables, data types, functions, and loops. You're well on your way to becoming a Python pro, and you have all the tools you need to start building our mini chat-GPT. The next step will be to introduce the basic principles of machine learning and natural language processing, as these are integral to creating an AI like GPT.

Lesson 5: Building a Simple Conversation Function

Now that we've covered the basics of loops, let's see how they apply in building a simple chatbot function. This will establish a foundational interaction before we delve into the machine learning aspect of our chatbot. We'll create this conversation function using the principles we've covered so far. This function will serve as the skeleton of our chatbot and will be updated later to integrate a machine learning model.

We'll first build a simple conversation function:

def chatbot():
    user_input = ""
    while user_input != "quit":
        user_input = input("You: ")
        print(f"Chatbot: You said '{user_input}'")

In this example, the chatbot takes user input and repeats it back. The conversation continues until the user types "quit". This is the simplest form of a chatbot. It doesn't have any understanding of the user's input nor any ability to generate relevant responses.

But we can make our chatbot a bit smarter by preparing some predefined responses to certain user inputs:

def simple_chatbot(user_input):
    responses = {
        "hello": "Hi, there! How can I help you today?",
        "how are you": "I'm an AI, so I don't have feelings, but thank you for asking!",
        "what's your name": "I'm a mini Chat-GPT developed by OpenAI."
    }

    # convert the user_input to lower case and remove any punctuation
    user_input = user_input.lower().strip(".!?")

    # get the response for the user_input or return a default one
    return responses.get(user_input, "I'm sorry, I didn't understand that.")

# testing the function
print(simple_chatbot("Hello!"))

def chatbot():
    user_input = ""
    while user_input != "quit":
        user_input = input("You: ")
        response = simple_chatbot(user_input)
        print(f"Chatbot: {response}")

chatbot()

This version of the chatbot has a set of predefined responses. It looks at the user's input and tries to match it with one of the predefined phrases. If a match is found, it returns the corresponding response. If not, it returns a default response.

However, real-life conversation scenarios are much more complex. It's impossible to anticipate and manually program responses for all possible user inputs. That's where machine learning comes into play. But don't worry, we're not diving into the complex parts just yet.

We'll start off the next part by gently introducing you to the basics of machine learning and natural language processing, the foundations for adding 'intelligence' to our chatbot. We'll take a look at how to train an AI model on a dataset, interpret the model's output, and begin to integrate the model into our chatbot function.

The journey from here gets even more exciting as we delve into the heart of AI. With the basics you've mastered so far, you're well-prepared to take this next step. So, take a deep breath, review what you've learned, and get ready for Part 3 – it's going to be a fantastic ride!

Wrapping Up Part 2

We've covered a lot in this part! You've learned about Python functions, their importance in writing clean and reusable code, and their potential role in AI. We delved into the concept of loops, another fundamental Python concept, and their critical role in handling repetitive tasks in programming and AI.

You've also started building the skeleton of our mini chat-GPT, understanding how to create a conversation function and defining some basic responses to user inputs. This gives you a foundational understanding of how chatbots interact with users.

To summarize:

Glossary:

  • Functions: A block of reusable code that performs a specific task. Functions allow for better organization, reusability, and readability in our code.
  • Loops: A programming concept that allows us to execute a block of code repeatedly. Loops are essential for handling large amounts of data and repetitive tasks.
  • For Loop: A type of loop in Python that iterates over a sequence (like a list, tuple, dictionary, set, or string).
  • While Loop: Another type of loop in Python that executes a set of statements as long as a certain condition is true.
  • Chatbot: A program designed to simulate conversation with human users, especially over the internet. Chatbots can be rule-based or use machine learning to provide more complex interactions.

Further Reading and Resources:

Ben's Thoughts

You've made great strides in your Python journey! Functions and loops are two critical concepts, not just in Python, but in almost all programming languages. Their effective use leads to efficient, maintainable, and scalable code. As you've seen, they're also essential in AI and chatbot development, allowing us to manage extensive datasets and automate repetitive tasks.

Remember, understanding and mastering the basics will set you up for success as we delve into more complex topics. Keep up the great work, and let's continue our exciting journey in Part 3!

[To be continued on part 3, you can leave your comments or feedback!]

AWS Cloud Credit for Research
Previous articleMastering the Art of Image Manipulation with DragGAN: Changing Reality, One Pixel at a Time
Next articleAI Impersonators: Unmasking the Strengths and Biases of Large Language Models
Benjamin Clarke, a New York-based technology columnist, specializes in AI and emerging tech trends. With a background in computer science and a Master's degree in Artificial Intelligence, Ben combines his technical expertise with an engaging storytelling style to bring complex topics to life. He has written for various publications and contributed to a variety of AI research projects. Outside of work, Ben enjoys exploring the vibrant New York City arts scene and trying out the latest gadgets and gizmos.

LEAVE A REPLY

Please enter your comment!
Please enter your name here