Large language models (LLMs) are a type of advanced AI technology that helps computers understand and generate human-like text. These models are built using a method called the transformer architecture, which is like a blueprint for creating them.
An example of an LLM is ChatGPT, created by OpenAI. LLMs have become crucial as AI technology is becoming more and more important in our lives. They work by learning from huge amounts of data, like a super-smart student studying from tons of books.
LLMs are like brainy computers made of many parts (called parameters) that can take an input and give an output in the form of human-like text. They're basically machines that understand and create text like humans do.
These models are trained using massive amounts of information from things like books, articles, and websites. They're part of a special kind of AI that's made to create text. LLMs have improved a lot since the invention of transformers by Google, making language processing better and more effective.
GPT 4 is the latest version from OpenAI. It's really good at creating accurate content based on what users ask for. They've focused on making it safer and more ethical. The creators of GPT 4 listened to feedback from users of previous models to make this one even better.
META (LLAMA) is a big AI model from Meta. They made it to make AI more accessible for researchers who might not have had the chance to study these models before. META (LLAMA) aims to help researchers improve AI and reduce issues like harmful content and bias. Right now, researchers can get access to META (LLAMA) through Meta.
Google's Palm is a new type of AI called Pathways. It's meant to be like a super AI that can do lots of tasks at once, unlike older AIs that could only do one thing at a time. Palm is designed to learn new things quickly, just like humans. For example, once you learn how to ride a bike, you don't forget easily.
AI is becoming more common in our daily lives. Here are some places where LLMs are making a big impact:
In this blog following topics will be covered
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Why Data Science?
What is Data Science?
In order to find effective statistics for their organizations, data scientists must have mastered the entire spectrum of the Data science life cycle and have the flexibility and understanding to maximize revenue at each stage of the process.
Who is a data scientist?
Data scientists have several definitions. Simply put, a data scientist is someone who adheres to the art of data science. The most popular term for ‘data scientist’ is Created by Patil and Jeff Hamperpatcher.
Data scientists are people who twist complex data problems with strong expertise in certain fields of science. They work with a number of components, including mathematics, statistics, and computer science (although not experts in these fields).
Job Trends in Data Science
Data science is forecast to grow over the next decade. It is a shocking fact that 90% of the world's data was created in just 2 years.
How to solve a problem in Data Science
1. Discovery:
2. Data preparation:
3. Model planning:
4. Model Building:
We will use various techniques such as association, classification, and clustering to create the model.
5. Operationalize:
At this stage, we will provide the final reports, summaries, code, and technical documentation of the project.
This step gives you a brief overview of the full project performance and other factors prior to full deployment.
6. Communicate the results:
At this stage, we will check whether we are reaching the goal we set in the initial stage.
We will communicate the findings and final results with the business team.
Data Science Components
1- Statistics:
Statistics are an important component of data science.
It is a method of collecting and analyzing large quantities of numerical data to obtain useful and meaningful statistics.
2-Visualization:
Visualization is the representation of data in areas such as maps and maps so that people can easily understand it.
This makes it easier to access extensive data.
The main goal of data visualization is to help identify patterns, trends, and foreigners in large data sets.
3-Machine Learning:
Machine learning acts as the backbone of data science.
This means training a machine to function as the human brain.
Different methods are used to solve problems.
With the help of machine learning, it is easy to make predictions about unexpected/future data.
4- Domain expertise:
Domain expertise is the specific knowledge or skills of a particular area. There are different areas of data science, for which we need field experts.
You cannot open the entire feature of an algorithm without proper knowledge of where the data is coming from.
The more we know about the problem, the more difficult it will be to solve. Also, a high level of expertise in the area will greatly improve the accuracy of the model you want to create.
This is why data scientists are generally well aware of the various areas in which they work.
They may not be experts, but a good data scientist usually focuses on multiple skills.
5- Data Engineering:
Includes data engineering, data recovery, storage, retrieval, and transfer. The key to understanding data engineering is in the field of engineering.
Engineers design and create things. Data engineers design and create tubes that transform and carry data, making it very useful for data scientists and other end users.
These pipelines take data from different sources and store it in a single warehouse.
6- Programming Languages:
Python:
Python is a high-level programming language provided by a wide-ranging library. This is a very popular language because most data scientists like it.
It offers expandable and generated data analysis libraries.
The best features of Python are dynamic type, functional, object-oriented, automated memory management, and practice.
R:
R is a popular programming language among data scientists that can be used on Windows, Unix, and Mac operating systems.
The best feature of the R language is data visualization, which is tough on Python, but it is more startup-friendly than Python.
R language is used to perform social analysis using subsequent data. Twitter language is used for data visualization and semantic clustering, and Google uses it to evaluate ad performance and make financial predictions.
Data Scientist job role?
The role of the data scientist is really a challenge! Although the skill packages and capabilities used by data scientists may vary as a skilled data scientist.
Be very innovative and unique in his approach to extracting data, gaining useful insights into solving business problems and challenges, and using various technologies intelligently.
Ability to find and create rich data sources.
A handful of experience in data mining techniques such as graph analysis, method finding, result perspectives, clustering, or statistical analysis.
Develop working models, systems, and tools using experimental and functional methods and techniques.
Analyze data from different sources and perspectives and find hidden statistics.
In this blog following topics will be covered
What are the Best Books for Data Science?
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You can give a guide to this book or for topics you may be missing while searching for online courses.
Do you want to become a Data Science Expert join the Live Training Program on Naresh I Technology .