Agentic AI is a rapidly growing area in AI development where large language models (LLMs) are given autonomy to reason, plan, and execute actions using tools. Frameworks like LangChain, CrewAI, AutoGPT, and OpenAgents empower developers to create intelligent agents capable of complex multi-step tasks. If you’re looking to experiment with these agentic frameworks, Google Colab … Read more
Large Language Models (LLMs) like GPT-3, LLaMA, and Falcon have revolutionized the fields of NLP and generative AI. But working with these models requires substantial compute power, memory, and careful environment setup. Fortunately, Google Colab provides a free and convenient way to experiment with these models in a browser-based Jupyter environment. This article walks you … Read more
Generative AI is revolutionizing the way we create content, from writing and art to music and code. With models like GPT, DALL·E, Stable Diffusion, and MusicGen, AI can now produce human-like text, generate stunning visuals, compose music, and even write functioning code. But how do you get started? The easiest way to begin experimenting is … Read more
LangChain is a powerful framework for building agentic AI systems powered by large language models (LLMs). With built-in support for tool use, memory, and reasoning, LangChain makes it easy to build autonomous agents that perform multi-step tasks. Google Colab is an ideal environment for prototyping LangChain agents. It offers free access to GPUs and a … Read more
AutoGPT is one of the most well-known open-source frameworks for creating autonomous AI agents. By leveraging the reasoning capabilities of large language models (LLMs) like GPT-4, AutoGPT allows you to set a goal and let the agent plan, execute, and iterate on tasks to achieve it. This makes AutoGPT ideal for research, automation, and complex … Read more
Google Colab is a free cloud-based platform that lets you write and execute Python code in a Jupyter Notebook environment. It’s especially popular among machine learning practitioners due to its simplicity, ease of access, and built-in support for GPU/TPU acceleration. But to get the most out of it, you need to set up your environment … Read more
If you’re working with Python for machine learning, data science, or AI development, chances are you’ve come across Google Colab and Jupyter Notebook. Both environments allow you to write and execute Python code in a notebook-style interface, complete with code cells, visualizations, and markdown annotations. But while they serve similar functions, they differ significantly in … Read more
Google Colab, short for Google Colaboratory, is a free cloud-based platform that allows users to write and execute Python code in a web-based Jupyter notebook environment. It’s especially popular among data scientists, machine learning enthusiasts, and educators because it comes with free access to GPUs and TPUs, pre-installed libraries, and seamless integration with Google Drive. … Read more
Google Colab, also known as Google Colaboratory, has become one of the most popular platforms for data scientists, machine learning practitioners, and Python enthusiasts. But why is Google Colab used? What makes it stand out from other environments like Jupyter Notebooks, Kaggle Kernels, or local IDEs? In this comprehensive guide, we will explore why Google … Read more
If you are new to data science or machine learning, you may have heard of Google Colab as a powerful tool for writing and executing Python code. But what exactly is Google Colab Python, and why has it become so popular among data scientists, developers, and researchers? This comprehensive guide will cover everything you need … Read more
