Understanding Embeddings and Matrices with the help of Sentiment Analysis and LLMs (Hands-On)

Imagine you’re browsing online and companies keep prompting you to rate and review your experiences. Have you ever wondered how these companies manage to process and make sense of the deluge of feedback they receive? Don’t worry! They don’t do it manually. This is where sentiment analysis steps in—a technology that analyzes text to understand the emotions and opinions expressed within.

Companies like Amazon, Airbnb, and others harness sentiment analysis to extract valuable insights. For example, Amazon refines product recommendations based on customer sentiments, while Airbnb analyzes reviews to enhance accommodations and experiences for future guests. Sentiment analysis silently powers these platforms, empowering businesses to better understand and cater to their customers’ needs.

Traditionally, companies like Amazon had to train complex models specifically for sentiment analysis. These models required significant time and resources to build and fine-tune. However, the game changed with Large Language Models like OpenAI’s ChatGPT, Google’s Gemini, Meta’s Llama, etc. which have revolutionized the landscape of natural language processing.

Now, with Large Language Models (LLMs), sentiment analysis becomes remarkably easier. LLMs are exceptionally skilled at understanding the sentiment of text because they have been trained on vast amounts of language data, enabling them to understand the subtleties of human expression.

Generated from Dall E 3
Continue reading “Understanding Embeddings and Matrices with the help of Sentiment Analysis and LLMs (Hands-On)”

Myth 1: LLMs Can Do Everything – We do not need Machine Learning.

Welcome to the kickoff of our new blog series dedicated to demystifying common misconceptions surrounding Language Models (LLMs) and generative Artificial Intelligence (AI). In this series, we aim to explore prevalent myths, clarify misunderstandings, and shed light on the nuanced realities of working with these cutting-edge technologies.

In recent years, LLMs like GPT-3, Gemini, LLama 3 have garnered significant attention for their impressive capabilities in natural language processing. However, with this growing interest comes a wave of misconceptions about what LLMs can and cannot do, often overlooking the vital role of traditional machine learning techniques in AI development.

Myth 1: LLMs Can Do Everything – We do not need Machine Learning.

In the rapidly evolving landscape of artificial intelligence (AI), there’s a prevalent myth that Large Language Models (LLMs) can autonomously handle all tasks, rendering traditional machine learning irrelevant. This oversimplified view is akin to saying, “If I have a hammer, everything must be a nail.” Let’s delve deeper into why this myth needs debunking.

Continue reading “Myth 1: LLMs Can Do Everything – We do not need Machine Learning.”

Building Generative AI and LLMs with CloudxLab

The world of Generative AI and Large Language Models (LLMs) is booming, offering groundbreaking possibilities for creative text formats, intelligent chatbots, and more. But for those new to AI development, the technical hurdles can be daunting. Setting up complex environments with libraries and frameworks can slow down the learning process.

CloudxLab is here to break down those barriers. We offer a unique platform where you can build Generative AI applications entirely within our cloud lab. This means you can:

  • Focus on Creativity, Not Configuration: No more wrestling with installations or environment setups. Our cloud lab provides everything you need to start building right away.
  • Seamless Learning Experience: Dive straight into the exciting world of Generative AI and LLMs. Our platform streamlines the process, letting you concentrate on understanding and applying these powerful technologies.
  • Accessible for All: Whether you’re a seasoned developer or a curious beginner, CloudxLab’s cloud environment makes Gen AI and LLM development approachable.
Continue reading “Building Generative AI and LLMs with CloudxLab”

Building a RAG Chatbot from Your Website Data using OpenAI and Langchain (Hands-On)

Imagine a tireless assistant on your website, ready to answer customer questions 24/7. That’s the power of a chatbot! In this post, we’ll guide you through building a custom chatbot specifically trained on your website’s data using OpenAI and Langchain. Let’s dive in and create this helpful conversational AI!

If you want to perform the steps along with the project in parallel, rather than just reading, check out our project on the same at Building a RAG Chatbot from Your Website Data using OpenAI and Langchain. You will also receive a project completion certificate which you can use to showcase your Generative AI skills.

Step 1: Grabbing Valuable Content from Your Website

We first need the gold mine of information – the content from your website! To achieve this, we’ll build a web crawler using Python’s requests library and Beautiful Soup. This script will act like a smart visitor, fetching the text content from each webpage on your website.

Here’s what our web_crawler.py script will do:

  1. Fetch the Webpage: It’ll send a request to retrieve the HTML content of a given website URL.
  2. Check for Success: The script will ensure the server responds positively (think status code 200) before proceeding.
  3. Parse the HTML Structure: Using Beautiful Soup, it will analyze the downloaded HTML to understand how the webpage is built.
  4. Clean Up the Mess: It will discard unnecessary elements like scripts and styles that don’t contribute to the core content you want for the chatbot.
  5. Extract the Text: After that, it will convert the cleaned HTML into plain text format, making it easier to process later.
  6. Grab Extra Info (Optional): The script can optionally extract metadata like page titles and descriptions for better organization.

Imagine this script as a virtual visitor browsing your website and collecting the text content, leaving behind the fancy formatting for now.

Let’s code!

import requests
from bs4 import BeautifulSoup
import html2text


def get_data_from_website(url):
    """
    Retrieve text content and metadata from a given URL.

    Args:
        url (str): The URL to fetch content from.

    Returns:
        tuple: A tuple containing the text content (str) and metadata (dict).
    """
    # Get response from the server
    response = requests.get(url)
    if response.status_code == 500:
        print("Server error")
        return
    # Parse the HTML content using BeautifulSoup
    soup = BeautifulSoup(response.content, 'html.parser')

    # Removing js and css code
    for script in soup(["script", "style"]):
        script.extract()

    # Extract text in markdown format
    html = str(soup)
    html2text_instance = html2text.HTML2Text()
    html2text_instance.images_to_alt = True
    html2text_instance.body_width = 0
    html2text_instance.single_line_break = True
    text = html2text_instance.handle(html)

    # Extract page metadata
    try:
        page_title = soup.title.string.strip()
    except:
        page_title = url.path[1:].replace("/", "-")
    meta_description = soup.find("meta", attrs={"name": "description"})
    meta_keywords = soup.find("meta", attrs={"name": "keywords"})
    if meta_description:
        description = meta_description.get("content")
    else:
        description = page_title
    if meta_keywords:
        meta_keywords = meta_description.get("content")
    else:
        meta_keywords = ""

    metadata = {'title': page_title,
                'url': url,
                'description': description,
                'keywords': meta_keywords}

    return text, metadata

Explanation:

The get_data_from_website function takes a website URL and returns the extracted text content along with any optional metadata. Explore the code further to see how it performs each step mentioned!

Step 2: Cleaning Up the Raw Text

Continue reading “Building a RAG Chatbot from Your Website Data using OpenAI and Langchain (Hands-On)”

How to build/code ChatGPT from scratch?

In a world where technology constantly pushes the boundaries of human imagination, one phenomenon stands out: ChatGPT. You’ve probably experienced its magic, admired how it can chat meaningfully, and maybe even wondered how it all works inside. ChatGPT is more than just a program; it’s a gateway to the realms of artificial intelligence, showcasing the amazing progress we’ve made in machine learning.

At its core, ChatGPT is built on a technology called Generative Pre-trained Transformer (GPT). But what does that really mean? Let’s understand in this blog.

In this blog, we’ll explore the fundamentals of machine learning, including how machines generate words. We’ll delve into the transformer architecture and its attention mechanisms. Then, we’ll demystify GPT and its role in AI. Finally, we’ll embark on coding our own GPT from scratch, bridging theory and practice in artificial intelligence.

How does Machine learn?

Imagine a network of interconnected knobs—this is a neural network, inspired by our own brains. In this network, information flows through nodes, just like thoughts in our minds. Each node processes information and passes it along to the next, making decisions as it goes.

Each knob represents a neuron, a fundamental unit of processing. As information flows through this network, these neurons spring to action, analyzing, interpreting, and transmitting data. It’s similar to how thoughts travel through your mind—constantly interacting and influencing one another to form a coherent understanding of the world around you. In a neural network, these interactions pave the way for learning, adaptation, and intelligent decision-making, mirroring the complex dynamics of the human mind in the digital realm.

Continue reading “How to build/code ChatGPT from scratch?”

Benefits and Challenges of Monolithic or Microservices Architecture

In the world of software development, the architectural choices made for building an application can have a profound impact on its scalability, maintainability, and overall success. Two prominent architectural patterns that have gained considerable attention in recent years are monolithic and microservices architecture. Each approach presents unique benefits and challenges, which we will explore in this blog post. By understanding the characteristics of both architectures, developers can make informed decisions when choosing the best option for their projects.

I. Monolithic Architecture

Monolithic architecture refers to a traditional approach where all components of an application are tightly coupled and packaged together into a single executable unit. Let’s delve into the benefits and challenges associated with this approach.

Benefits of Monolithic Architecture

Continue reading “Benefits and Challenges of Monolithic or Microservices Architecture”

GPT 4 and its advancements over GPT 3

The field of natural language processing has witnessed remarkable advancements over the years, with the development of cutting-edge language models such as GPT-3 and the recent release of GPT-4. These models have revolutionized the way we interact with language and have opened up new possibilities for applications in various domains, including chatbots, virtual assistants, and automated content creation.

What is GPT?

GPT is a natural language processing (NLP) model developed by OpenAI that utilizes the transformer model. Transformer is a type of Deep Learning model, best known for its ability to process sequential data, such as text, by attending to different parts of the input sequence and using this information to generate context-aware representations of the text.

What makes transformers special is that they can understand the meaning of the text, instead of just recognizing patterns in the words. They can do this by “attending” to different parts of the text and figuring out which parts are most important to understanding the meaning of the whole.

For example, imagine you’re reading a book and come across the sentence “The cat sat on the mat.” A transformer would be able to understand that this sentence is about a cat and a mat and that the cat is sitting on the mat. It would also be able to use this understanding to generate new sentences that are related to the original one.

GPT is pre-trained on a large dataset, which consists of:

Continue reading “GPT 4 and its advancements over GPT 3”

Scholarship Test for PG Certificate in Data Science, AI/ML from IIT Roorkee. Earn Rs 75,000 Discount in One Hour.

We all know what’s ruling technology right now.

Yes, it is Artificial Intelligence, Machine Learning, Data Science, and Data Engineering. 

Therefore, now is the time to propel your Data Science career. Look no further because you can enroll for a PG Certificate Course in Data Science from IIT Roorkee. To make enrolment easy for you, here’s a Free Scholarship Test you can take and earn discounts up to Rs.75,000!

The Scholarship Test is a great opportunity for you to earn discounts. There are 50 questions that you have to attempt in one hour.
Each question you answer correctly earns you a discount of Rs 1000, and you can earn a maximum discount of Rs 75,000! (50/50 rewards you with an additional 25000 scholarship)

This Scholarship Test for the Data Science course is a great way to challenge yourself in basic aptitude and basic programming questions and to earn a massive discount on the course fees. 

The PG Certificate course from IIT Roorkee covers all that you need to know in technology right now. You will learn the architecture of ChatGPT, Stable Diffusion, Machine Learning, Artificial Intelligence, Data Science, Data Engineering and more! The course will be delivered by Professors from IIT Roorkee and industry experts and follows a blended mode of learning. Learners will also get 365 days of access to cloud labs for hands-on practice in a gamified learning environment. 

Data Scientists, Data Engineers, Data Architects are some of the highly sought after professionals today. With businesses and life-changing innovations being data driven in every domain, the demand for expertise in Deep Learning, Machine Learning is on the rise. This PG Certificate Course gives you the skills and knowledge required for a propelling career in Data Science. 

So what are you waiting for? Seats to the PG Certificate Course in Data Science from IIT Roorkee are limited. Take the Scholarship Test, earn discounts, and enroll now.

Link to the Scholarship Test is here.

Details about the PG Certificate Course in AI, Machine Learning, and Data Science are here.

Impact of AI on various industries in 2024

Artificial intelligence (AI) is having a profound impact on many different industries and is transforming the way businesses and organizations operate and serve their customers. With the help of AI, organizations are able to automate complex processes, make better predictions and decisions, and provide more personalized and efficient services to their customers.

One of the key areas where AI is making a big difference is in the field of healthcare. AI algorithms are being used to analyze medical data, such as images, records, and biomarkers, and to make more accurate predictions about the likelihood of diseases and the effectiveness of treatments. This can help healthcare providers to diagnose and treat patients more effectively, and improve the overall quality of care.

Continue reading “Impact of AI on various industries in 2024”

I’m from the telecom industry, should I learn Data Science and AI?

In the telecom industry, the use of AI and data science is becoming increasingly important for companies that want to stay competitive and deliver the best possible services to their customers.

Only by leveraging the power of AI and data science, telecom companies can gain valuable insights into their operations and make data-driven decisions that can help them improve efficiency, reduce costs, and develop new products and services.

One key area where AI and data science can help telecom companies is in network optimization. By analyzing vast amounts of data from network sensors and other sources, AI algorithms can identify patterns and anomalies that can indicate where the network is underperforming or prone to failure. This can help telecom companies take proactive steps to improve network reliability and reduce downtime, leading to a better overall customer experience.

Continue reading “I’m from the telecom industry, should I learn Data Science and AI?”