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Lecture: Data Science Concepts, Tools, and Math: Bayesian Math and Generative AI Programs

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How Bayesian Machine Learning Powers ChatGPT coda.io⁠

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Baysian Models are prediction-building engines. They work by applying statistical mathematics in PYTHON Algorithms.

Hello, my dear students! Today, we'll embark on a journey to explore the fascinating world of Data Science, its concepts, tools, and the mathematical foundation required to build generative AI programs. We'll give special attention to Bayesian Math, a powerful technique with wide-ranging applications in modern AI.

Student Sara’s Narrative Continued:

As I continue my journey in the world of Data Science, I am more excited than ever to apply these concepts, tools, and mathematical foundations in practice.

I have come to appreciate the importance of Bayesian Math in building generative AI programs and making predictions in uncertain environments. As someone who is passionate about creating remarkable AI applications, I realize that understanding and mastering Bayesian Math is crucial for my success.

In my upcoming projects, I plan on utilizing Bayes' theorem to update my beliefs based on new evidence. Our software application will record its learning in the ML Ops Model called the Baysian Model. This will be particularly useful in situations where I need to make predictions in real-time, such as when working with real-world datasets or live user interactions. By applying Bayesian Math, I can ensure that my AI programs can

Accept hetrogenous types of inputs

adapt and provide accurate predictions even when faced with incomplete or uncertain information.

A question to consider: {potential mid term test question}

What are the differences between a Database and a Generative AI Language Model:

A database must be given precise instructions on what to search for.

The data to answer that query must reside with the database.

Generative AI Language Model:

We can ask vague questions. I can ask hypothetical questions about things which have not happen yet. Or even impossible things.

We can ask the Model speculate: We ask the model to make up information. To project current understanding into likely paths of development of future outcomes.

Furthermore, I am eager to explore the world of generative models, such as Bayesian networks, Gaussian mixture models, and hidden Markov models. These models have the potential to generate new data that resembles the input data, opening up a world of possibilities for data augmentation, anomaly detection, and simulation. By incorporating these generative models into my projects, I can create AI applications that not only understand the underlying patterns in the data but also generate meaningful and realistic outputs.

To develop my skills in Bayesian Math and Data Science, I will dedicate time each day to learning new concepts, algorithms, and tools.

I plan on attending workshops, taking online courses, and engaging with fellow data science enthusiasts to further deepen my understanding of these topics. In addition, I will actively seek opportunities to collaborate on projects where I can apply my knowledge to real-world problems, helping me gain valuable experience and insights.

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Canadian Artificial Intelligence Association (CAIAC) coda.io⁠

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As I progress in my data science journey, I will also be mindful of the ethical implications of my work. I understand that AI applications can have a profound impact on society, and I want to ensure that I contribute positively to this rapidly evolving field.

By staying up-to-date with the latest research and best practices, I can make informed decisions about my work and its potential consequences.

In conclusion, I am eager to apply my knowledge of Data Science, Bayesian Math, and generative AI programs in practice.

By continuously learning and collaborating with others in the field, I am confident that I can create AI applications that make a meaningful difference in the world. As Student Sara, I embrace the challenges and opportunities that lie ahead and look forward to seeing how my passion for Data Science and Bayesian Math translates into real-world impact.

Data Science: The Art of Transforming Raw Data into Knowledge, Wisdom and Insight

Imagine the universe of information as a giant puzzle. Each piece of data is like a jigsaw piece, waiting to be put together. Data Science is the art of assembling these pieces to reveal meaningful insights and uncover hidden patterns. It's a multidisciplinary field that combines mathematics, statistics, computer science, and domain (specific industry or business) expertise to extract knowledge from data.

The Modern Alchemist turns the lead of raw data into the GOLD of insights and patterns that we can monetize and make money from.

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A. Data Science Concepts

Data Collection: The first step in any data science project is gathering raw data. It could be structured, semi-structured, or unstructured. We can obtain data from various sources like databases, APIs, web scraping, or direct user input.

Data Preprocessing: Before diving into analysis, we might need to clean our data. It's like untangling a messy ball of yarn – removing duplicates, filling in missing values, and transforming the data into a more usable format.

Exploratory Data Analysis: Here, we perform a preliminary investigation of the data, examining its structure and properties. We can use visualization techniques to better understand the relationships between variables.

Feature Engineering: We take raw data and transform it into meaningful features that can be fed into our AI algorithms. This step may involve scaling, normalization, or even creating new features based on existing data.

scaling: what happens when millions of users start accessing our product?

normalization: trying to make different data sets look similar enough that we can do meaning data comparisons on them

features: are the delivered abilities of our product that customers use to do their work: customers buy our product to get the use of the Features.

Model Building: Once our data is prepped, we move on to build and train machine learning models using Tools such as PyTORCH. These models learn from the data, identifying patterns and making predictions: these outcomes are accomplished with use of Baysian Mathematical Models.

How PyTorch uses and applies Bayesian mathematics, specifically through the Pyro library. Pyro is a companion library for PyTorch that enables probabilistic programming on neural networks written in PyTorch [1].

Probabilistic programming allows us to incorporate uncertainty in our deep learning models, leading to better predictions and improved decision-making.

In this lecture, we will go through a simple example of using Pyro to perform Bayesian inference in a linear regression model.

First, let's import the necessary libraries:

import torch

import pyro

import pyro.distributions as dist

Next, we define our linear regression model, which is a simple function that takes input x, and returns y, with added noise:

def linear_regression(x, w, b, noise_stddev):

y = w * x + b

return y + torch.normal(0, noise_stddev, size=y.shape)

Now, let's create our prior distributions for the model parameters. We will use the Normal distribution for both w (weight) and b (bias), and a HalfNormal distribution for the noise standard deviation:

weight: in AI neural networks: how much your model likes to make certain connections

bias:

def model(x, y):

w_prior = dist.Normal(0, 1)

b_prior = dist.Normal(0, 1)

noise_stddev_prior = dist.HalfNormal(1)

w = pyro.sample("w", w_prior)

b = pyro.sample("b", b_prior)

noise_stddev = pyro.sample("noise_stddev", noise_stddev_prior)

y_hat = linear_regression(x, w, b, noise_stddev)

pyro.sample("y", dist.Normal(y_hat, noise_stddev), obs=y)

The model function defines our prior beliefs about the parameters and the likelihood function of the observed data.

For Bayesian inference ( to make a predictive guess), we need a guide function, which is an approximation of the posterior distribution.

We will use the same distribution families as our priors, but with learnable parameters:

def guide(x, y):

w_loc = pyro.param("w_loc", torch.tensor(0.0))

w_scale = pyro.param("w_scale", torch.tensor(1.0), constraint=dist.constraints.positive)

b_loc = pyro.param("b_loc", torch.tensor(0.0))

b_scale = pyro.param("b_scale", torch.tensor(1.0), constraint=dist.constraints.positive)

noise_stddev_loc = pyro.param("noise_stddev_loc", torch.tensor(1.0), constraint=dist.constraints.positive)

w = pyro.sample("w", dist.Normal(w_loc, w_scale))

b = pyro.sample("b", dist.Normal(b_loc, b_scale))

noise_stddev = pyro.sample("noise_stddev", dist.HalfNormal(noise_stddev_loc))

Finally, we perform inference using stochastic variational inference (SVI) to learn the posterior distribution of the model parameters:

from pyro.infer import SVI, Trace_ELBO

# Generate synthetic data

x = torch.randn(100)

y = 3 * x + 2 + torch.normal(0, 0.5, size=x.shape)

# Set up the optimizer and the inference algorithm

optimizer = pyro.optim.Adam({"lr": 0.01})

svi = SVI(model, guide, optimizer, loss=Trace_ELBO())

# Train the model

num_iterations = 1000

for i in range(num_iterations):

svi.step(x, y)

After learning the posterior distribution, we can use the learned parameters to make predictions and quantify uncertainty.

This example demonstrates how PyTorch, with the help of the Pyro library, can utilize Bayesian mathematics to build powerful and interpretable models.

References:

[1]

Making Your Neural Network Say “I Don't Know”⁠

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[2]

PyTorch Tutorial: How to Develop Deep Learning Models ...⁠

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[3]

Dropout as Regularization and Bayesian Approximation⁠

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B. Data Science Tools

There are countless tools at our disposal in the data science world, from programming languages like Python and R to visualization libraries like Matplotlib and Seaborn. We also have machine learning libraries like Scikit-learn, TensorFlow, and PyTorch.

These tools help us manipulate data and develop powerful AI algorithms.

Bayesian Math: A Key to Unlocking Generative AI Programs

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Student Lab Workbook: Bayes' Theorem Fundamentals coda.io⁠

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Bayesian Math is a branch of probability theory that deals with updating probabilities based on new evidence. Which causes are most likely to produce which kinds of Outcomes? It's named after the Reverend Thomas Bayes, who formulated the famous Bayes' theorem.

“In Bayes, We Trust”

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Bayesian methods are instrumental in developing generative AI programs and making predictions in uncertain environments.

Bayes' Theorem

Bayes' theorem helps us update our beliefs based on new evidence. In simple terms, it tells us how to combine our prior knowledge (prior probability) with new data (likelihood) to obtain an updated probability (posterior probability).

The theorem is expressed as:

P(A|B) = (P(B|A) * P(A)) / P(B)

Where:

P(A|B): The probability of event A happening, given that event B has occurred (posterior probability).

P(B|A): The probability of event B happening, given that event A has occurred (likelihood).

P(A): The probability of event A happening (prior probability).

P(B): The probability of event B happening (marginal probability).

Using Bayesian Math in Building the Generative AI Language Model.

Generative models, such as Bayesian networks, Gaussian mixture models, and hidden Markov models, are built on the principles of Bayesian Math.

These models can generate new data that resembles the input data. One popular example of a generative AI program is the ChatGPT model, which uses Bayesian methods to generate human-like text.

By incorporating Bayesian Math, generative AI models can learn the underlying structure of the data and make predictions even when faced with uncertainty.

This powerful technique helps AI models adapt to new information and become more robust in their predictions.

Conclusion

Data Science and Bayesian Math are essential components in your Toolbox as an AI Systems Analysts, given the job by your employers of building Generative AI Language Models.

By understanding the concepts and tools of Data Science and harnessing the power of Bayesian Math, we can create generative AI programs capable of remarkable feats.

One of the Big Wins in using Baysian Models to surface key trends in own knowledge sets, is that as human beings, we have “mental filters and limiters” than block our awareness of the totalilty of the information around us. Our models can surface to our awareness facts, trends, occurances, which could be quite important to us, but which our mental filters are preventing us from seeing.

As you venture into the world of AI, remember to appreciate the beauty and elegance of the math that makes it all possible.

Like a masterful composer, weave these techniques together to create your own symphony of artificial intelligence.

And above all, never stop exploring and learning, for the universe of Data Science and AI is vast and ever-expanding.

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