#LangChain

Question count - 0     Last updated    - V4 (19-Nov-2024)

LangChain	LangChain is a framework designed to develop applications powered by large language models (LLMs). It simplifies the process of building complex AI systems by connecting LLMs to external data and other computational resources, overcoming limitations of LLMs working solely on input prompts.
Core components	Document Loaders: Easily ingest data from various sources (PDFs, websites, databases, etc.) making it accessible to the LLM. Text Splitters: Break down large documents into smaller, manageable chunks suitable for LLM processing, respecting context window limitations. Embeddings: Convert text into numerical representations (vectors) that capture semantic meaning, crucial for similarity search. Vectorstores: Efficiently store and search vector embeddings, enabling quick retrieval of relevant information. Retrievers: Fetch the most pertinent information from the vectorstore based on user queries, supplying context to the LLM. LLMs: LangChain supports multiple LLMs, providing flexibility to choose the best model for specific tasks. Prompt Templates: Create and manage prompts effectively, enabling dynamic input and output formatting for consistent LLM responses.
Why use LangChain framework?	Simplified Development: Abstracts away low-level complexities, allowing developers to focus on application logic. Data Integration: Seamlessly connects LLMs with external data sources, enhancing capabilities and accuracy. Enhanced Functionality: Enables building applications that go beyond simple question-answering. Improved Context Management: Handles context window limitations of LLMs by efficient data management. Model Agnosticism: Supports multiple LLMs, providing flexibility and adaptability.
How to get started with LangChain?	LangChain offers comprehensive documentation and tutorials to help you begin building your LLM applications. It's an open-source project with a vibrant community, providing ample support and resources.

Question

Option A

Option B

Option C

Option D

#Reinforcement_Learning

Question count - 0     Last updated    - V4 (12-Nov-2024)

Reinforcement Learning

Learns by interacting with its environment and receiving rewards or penalties for its actions Used to train robots and game-playing agents Learns to take actions that maximize its rewards over time

Question

Option A

Option B

Option C

Option D

#Semi_Supervised_Learning

Question count - 0     Last updated    - V4 (12-Nov-2024)

Question

Option A

Option B

Option C

Option D

#Unsupervised Learning

Question count - 0     Last updated    - V4 (12-Nov-2024)

Unsupervised Learning

Trained on unlabeled data (data without known outputs) Tries to find patterns and relationships in the data on its own Used for clustering tasks (e.g., grouping customers together based on their purchase history) and dimensionality reduction tasks (e.g., reducing the number of features in a dataset without losing too much information)

Question

Option A

Option B

Option C

Option D

#Supervised_Learning

Question count - 0     Last updated    - V4 (12-Nov-2024)

Supervised Learning

Trained on labeled data (data with known outputs) Learns to predict the output for new data based on patterns learned from the training data Used for classification (e.g., predicting whether an email is spam or not) and regression tasks (e.g., predicting the price of a house)

Question

Option A

Option B

Option C

Option D

#NumPy

Question count - 0     Last updated    - V4 (12-Nov-2024)

Question

Option A

Option B

Option C

Option D