Escape Velocity
Escape velocity refers to the velocity needed for an object to escape the gravitational pull and move into space. Now, let's draw an analogy with learning, up-skilling, and breaking barriers in personal development.
Pre-Industrial Revolution
Each tool or technology utilized today has emerged from a necessity within human civilization to simplify day-to-day life. As a result, PreProd Opnsrc Technology Blogs have been launched, drawing insights from all the industrial revolutions humanity has experienced thus far.
First-Industrial Revolution
As you embark on your career in this modern era of industry, reflect on the innovators who paved the way during the First Industrial Revolution.
Second Industrial Revolution
The Second Industrial Revolution stands as one of the most transformative periods in human history, marked by unparalleled technological advancements, economic growth, and societal change.
Third Industrial Revolution
The Third Industrial Revolution marks a significant shift from its predecessors because it isn't driven by one big discovery like steam power or electricity, but by the coming together of several groundbreaking technologies. In our blog journey, we're about to dive deep into this revolution.
Fourth Industrial Revolution
Fourth Industrial Revolution, also known as the "Industry 4.0" or 4IR, is marked by a fusion of the physical and digital worlds through interconnected digital technologies.
Fifth Industrial Revolution
5IR builds upon the digital innovations of the Fourth Industrial Revolution (4IR), integrating advanced technologies such as artificial intelligence (AI), robotics, blockchain, and biotechnology with human-centered approaches to create a collaborative ecosystem.
Comparing All IR
The four Industrial Revolutions progressed from mechanization (1IR), through mass production and electrification (2IR), to the rise of electronics and automation (3IR), culminating in digital and cyber-physical systems integration (4IR).
Stages of AI
The progression of Artificial Intelligence (AI) can be described through five distinct stages, each representing significant advancements in capability and complexity.
CLASSIFICATION OF AI ALGORITHMS
Machine learning algorithms can be categorized into several types based on their learning style and the type of problem they are designed to solve.
TOP 50 AIML JARGONS
We have listed the most important 50 AIML jargons for you to read. There are many more, the listed ones are important as they cover a wide range of concepts, techniques, and tools essential to understanding and working in the field of AI and machine learning.
Top 50 AIML ALGORITHMS
Top 50 AI/ML algorithms involves a broad spectrum of techniques from various branches of AI and ML, including supervised learning, unsupervised learning, reinforcement learning, deep learning, and more.
TOP 10 PROGRAMMING LANGUAGES FOR AIML
The landscape of programming languages for Artificial Intelligence (AI) and Machine Learning (ML) is diverse, with each language offering unique strengths and capabilities.
TOP 50 PYTHON LIBRARIES FOR AIML
Python libraries cover a wide range of tasks in AI and machine learning, from data manipulation and visualization to deep learning and reinforcement learning, making them essential tools for researchers and practitioners in the field.
HARDWARE & SOFTWARE REQUIREMENTS FOR AIML
Hardware and software components are critical for different stages of AI development and deployment, including data preprocessing, model training, inference, and real-time processing. The choice of hardware depends on the specific requirements of the AI tasks and the desired balance between performance, cost, and flexibility.
5 Vs OF DATA
The "5 Vs of Data" framework is commonly used to describe the key dimensions of big data. Understanding these dimensions helps in managing, analyzing, and leveraging large datasets effectively.
DATA "THE NEWLY FOUND OIL"
In the digital age, the phrase "data is the newly discovered oil" has become a mantra among technologists and business leaders alike. This analogy underscores the immense value of data in driving modern economies, much like oil did in the 20th century.
TOP 10 MOST PROMISING TECHNOLOGIES OF FUTURE
These technologies represent the forefront of innovation and are poised to transform industries and society over the next decade. As they mature, they will drive economic growth, enhance quality of life, and address global challenges such as climate change and healthcare.
Data Engineer
Data engineering is a dynamic and rewarding field that sits at the intersection of technology and innovation. Whether you’re just starting out or looking to pivot in your career, the skills and knowledge you gain as a data engineer will open doors to endless opportunities.
Data Scientist
Data science is a thrilling field that combines curiosity, technical skills, and creative problem-solving. Whether you’re just starting or looking to shift gears in your career, the journey to becoming a data scientist is both challenging and rewarding.
MLOps Engineer
MLOps engineers are the architects of AI, ensuring that the machine learning models built by data scientists are reliable, scalable, and integrated seamlessly into production systems.
Data Engineer
Data engineering is a dynamic and rewarding field that sits at the intersection of technology and innovation. Whether you’re just starting out or looking to pivot in your career, the skills and knowledge you gain as a data engineer will open doors to endless opportunities.
XAI
XAI stands for "Explainable AI", i.e. making AI models transparent to all. By making AI models transparent, we empower users and developers alike, ensuring that AI systems are not only smarter but also fairer and more accountable.
Top 50 Numpy commands for Python
NumPy is the backbone of numerical computing in Python, a must-know for anyone diving into data science, machine learning, or scientific computing.
Top 50 Pandas commands for Python
Welcome to the exciting world of Pandas, the go-to library for data manipulation and analysis in Python. Whether you're a college student dipping your toes into data science or a seasoned professional, these 50 essential Pandas commands will help you become a data wrangling wizard.
Involvement - The Key to Mastery
In the rapidly evolving landscape of education and professional development, this age-old wisdom from Benjamin Franklin resonates more than ever.
100 Tools used for Data Engineering
List of 100 tools used for data engineering.
Git ~ GitHub ~ GitLab
Understand what Git, GitHub and GitLab does. Choose your champion tool based on your quest’s requirements. Whether you’re a lone coder, a collaborative team, or a DevOps powerhouse, there’s a tool ready to guide you on your path to digital mastery.
Key AI Stakeholders
Key stakeholders for an AIML project encompass a broad range of roles, each contributing to the project's success from conception to deployment and beyond.
Sprinting with AI team
In the fast-paced world of Artificial Intelligence, agility isn't just a buzzword—it's a necessity. Agile methodologies offer a structured yet flexible approach to developing AI solutions that can rapidly adapt to changing requirements and technologies.
Agile Mindset
Welcome to the world of Agile Mythology—a realm where flexibility, collaboration, and continuous improvement guide the creation of groundbreaking technology.
Investing in Agile excellence
Imagine a master chef preparing a gourmet meal. It’s not just the ingredients that matter, but the precise investment of time, skill, and resources that transform a collection of raw materials into a culinary masterpiece. Similarly, for employers venturing into the world of Agile mythology to build cutting-edge technology products, the investment of time and money in getting the team ready is crucial.
When agile get awry
Imagine building a state-of-the-art spaceship. You've got a top-tier crew, the best materials, and a groundbreaking design. But what happens if the team decides to ignore the blueprint and work haphazardly? This is precisely what occurs when an Agile team doesn't adhere to the principles and guidelines of Agile methodology. Welcome to a cautionary tale of chaos, missed opportunities, and the downfall of technology projects when Agile rules are disregarded.
Linear Regression: Introduction
Linear regression is the magic wand that draws the best-fit line through a scatterplot of data points. It's a way to model the relationship between two variables by fitting a linear equation to observed data.
Linear Regression: Constraints
Linear regression is a powerful and widely used statistical technique, but it comes with several constraints and assumptions that, if violated, can lead to misleading or incorrect results. Understanding these constraints is crucial for correctly applying linear regression and interpreting its results.
Unable to reach production
Artificial Intelligence and Machine Learning (AI/ML) are revolutionizing industries by offering unprecedented insights and automation. However, despite the buzz, a startling majority of AI/ML projects fail to make it to production. Understanding the reasons behind these failures can provide valuable lessons for aspiring data scientists, seasoned professionals, and businesses alike.
Claim it, know it, prove it.
In today's job market, there's a curious phenomenon unfolding. Picture this: a CV laden with grand achievements, cutting-edge projects, and skills that would make even the most seasoned professional nod in admiration. Yet, when the curtain rises and the interview spotlight shines, the illusion dissipates.
MySQL: Getting Started - Windows
This article will enable you to install MySQL on windows OS and perform CRUD operations.
MySQL: Getting Started - Linux
This article will enable you to install MySQL on linux OS and perform CRUD operations.
MySQL: Getting Started - MacOs
This article will enable you to install MySQL on windows OS and perform CRUD operations.
MySQL: Getting Started - Windows
Comparing DevOps, MLOps, and AIOps: A short read for for College Students, Graduates, and Professionals. Let’s dive into an innovative and technical comparison that caters to college students, fresh graduates, and seasoned professionals.
Introduction to basics of Tensorflow, Keras and PyTorch - Part 1
In the 7 part series on Tensorflow, Keras, Pytorch, Part 1 talks about Tensor and types of Tensor which is regarded as the basic building block of tensorflow
Introduction to basics of Tensorflow, Keras and PyTorch - Part 2
Part 2 of the series discusses on the concept of computational graphs which basically highlights the difference between tensorflow version 1 and 2
Introduction to basics of Tensorflow, Keras and PyTorch - Part 3
In Part 3 of the series, the concept of " Automatic Differentiation " on a fully connected network using tensorflow's native code has been discussed highlighting how tensorflow works at the backend without the use of keras
Introduction to basics of Tensorflow, Keras and PyTorch - Part 4
In Part 4 of the series, Why and how keras can be leveraged as a wrapper to tensorflow has been discussed
