Anthropicโs AI assistant Claude learns to search the web
Anthropic has announced its AI assistant Claude can now search the web, providing users with more up-to-date and relevant responses.
This integration of web search functionality means Claude can now access the latest information to expand its knowledge base beyond its initial training data.
A key feature of this update is the emphasis on transparency and fact-checking. Anthropic highlights that โWhen Claude incorporates information from the web into its responses, it provides direct citations so you can easily fact check sources.โ
Anthropic has announced its AI assistant Claude can now search the web, providing users with more up-to-date and relevant responses.
This integration of web search functionality means Claude can now access the latest information to expand its knowledge base beyond its initial training data.
A key feature of this update is the emphasis on transparency and fact-checking. Anthropic highlights that โWhen Claude incorporates information from the web into its responses, it provides direct citations so you can easily fact check sources.โ
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7 level of writing Python Dictionary
Level 1: Basic Dictionary Creation
Level 2: Accessing and Modifying values
Level 3: Adding and Removing key Values Pairs
Level 4: Dictionary Methods
Level 5: Dictionary Comprehensions
Level 6: Nested Dictionary
Level 7: Advanced Dictionary Operations
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Level 1: Basic Dictionary Creation
Level 2: Accessing and Modifying values
Level 3: Adding and Removing key Values Pairs
Level 4: Dictionary Methods
Level 5: Dictionary Comprehensions
Level 6: Nested Dictionary
Level 7: Advanced Dictionary Operations
I have curated the best interview resources to crack Python Interviews ๐๐
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Artificial intelligence can change your career by 180 degrees! ๐
Here's how you can start with AI engineering with zero experience!
The simplest definition of artificial intelligence|
Artificial intelligence (AI) is a part of computer science that creates smart systems to solve problems usually needing human intelligence.
AI includes tasks like recognizing objects and patterns, understanding voices, making predictions, and more.
Step 1: Master the prerequisites
Basics of programming
Probability and statistics essentials
Data structures
Data analysis essentials
Step 2: Get into machine learning and deep learning
Basics of data science, an intersection field
Feature engineering and machine learning
Neural networks and deep learning
Scikit-learn for machine learning along with Numpy, Pandas and matplotlib
TensorFlow, Keras and PyTorch for deep learning
Step 3: Exploring Generative Adversarial Networks (GANs)
Learn GAN fundamentals: Understand the theory behind GANs, including how the generator and discriminator work together to produce realistic data.
Hands-on projects: Build and train simple GANs using PyTorch or TensorFlow to generate images, enhance resolution, or perform style transfer.
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Step 6: Introduction to LangChain
Understand LangChain: Familiarize yourself with LangChain, a framework designed to build applications that combine language models with external knowledge and capabilities.
Build applications: Use LangChain to develop applications that interactively use language models to process and generate information based on user queries or tasks.
Step 7: Leveraging Vector Databases
Basics of vector databases: Understand what vector databases are and why they are crucial for managing high-dimensional data typically used in AI models.
Tools and technologies: Learn to use vector databases like Milvus, Pinecone, or Weaviate, which are optimized for fast similarity search and efficient handling of vector embeddings.
Practical application: Integrate vector databases into your projects for enhanced search functionalities
Step 8: Exploration of Retrieval-Augmented Generation (RAG)
Learn the RAG approach: Understand how RAG models combine the power of retrieval (extracting information from a large database) with generative models to enhance the quality and relevance of the outputs.
Practical applications: Study case studies or research papers that showcase the use of RAG in real-world applications.
Step 9: Deployment of AI Projects
Deployment tools: Learn to use tools like Docker for containerization, Kubernetes for orchestration, and cloud services (AWS, Azure, Google Cloud) for deploying models.
Monitoring and maintenance: Understand the importance of monitoring AI systems post-deployment and how to use tools like Prometheus, Grafana, and Elastic Stack for performance tracking and logging.
Step 10: Keep building
Implement Projects and Gain Practical Experience
Work on diverse projects: Apply your knowledge to solve problems across different domains using AI, such as natural language processing, computer vision, and speech recognition.
Contribute to open-source: Participate in AI projects and contribute to open-source communities to gain experience and collaborate with others.
Hope this helps you โบ๏ธ
Here's how you can start with AI engineering with zero experience!
The simplest definition of artificial intelligence|
Artificial intelligence (AI) is a part of computer science that creates smart systems to solve problems usually needing human intelligence.
AI includes tasks like recognizing objects and patterns, understanding voices, making predictions, and more.
Step 1: Master the prerequisites
Basics of programming
Probability and statistics essentials
Data structures
Data analysis essentials
Step 2: Get into machine learning and deep learning
Basics of data science, an intersection field
Feature engineering and machine learning
Neural networks and deep learning
Scikit-learn for machine learning along with Numpy, Pandas and matplotlib
TensorFlow, Keras and PyTorch for deep learning
Step 3: Exploring Generative Adversarial Networks (GANs)
Learn GAN fundamentals: Understand the theory behind GANs, including how the generator and discriminator work together to produce realistic data.
Hands-on projects: Build and train simple GANs using PyTorch or TensorFlow to generate images, enhance resolution, or perform style transfer.
Step 4: Get into Transformers architecture
Grasp the basics: Study the Transformer architecture's key concepts, including attention mechanisms, positional encodings, and the encoder-decoder structure.
Implementations: Use libraries like Hugging Faceโs Transformers to experiment with different Transformer models, such as GPT and BERT, on NLP tasks.
Step 5: Working with Pre-trained Large Language Models
Utilize existing models: Learn how to leverage pre-trained models from libraries like Hugging Face to perform tasks like text generation, translation, and sentiment analysis.
Fine-tuning techniques: Explore strategies for fine-tuning these models on domain-specific datasets to improve performance and relevance.
Step 6: Introduction to LangChain
Understand LangChain: Familiarize yourself with LangChain, a framework designed to build applications that combine language models with external knowledge and capabilities.
Build applications: Use LangChain to develop applications that interactively use language models to process and generate information based on user queries or tasks.
Step 7: Leveraging Vector Databases
Basics of vector databases: Understand what vector databases are and why they are crucial for managing high-dimensional data typically used in AI models.
Tools and technologies: Learn to use vector databases like Milvus, Pinecone, or Weaviate, which are optimized for fast similarity search and efficient handling of vector embeddings.
Practical application: Integrate vector databases into your projects for enhanced search functionalities
Step 8: Exploration of Retrieval-Augmented Generation (RAG)
Learn the RAG approach: Understand how RAG models combine the power of retrieval (extracting information from a large database) with generative models to enhance the quality and relevance of the outputs.
Practical applications: Study case studies or research papers that showcase the use of RAG in real-world applications.
Step 9: Deployment of AI Projects
Deployment tools: Learn to use tools like Docker for containerization, Kubernetes for orchestration, and cloud services (AWS, Azure, Google Cloud) for deploying models.
Monitoring and maintenance: Understand the importance of monitoring AI systems post-deployment and how to use tools like Prometheus, Grafana, and Elastic Stack for performance tracking and logging.
Step 10: Keep building
Implement Projects and Gain Practical Experience
Work on diverse projects: Apply your knowledge to solve problems across different domains using AI, such as natural language processing, computer vision, and speech recognition.
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ChatGPT is a powerful tool to streamline and enhance your SEO efforts, but itโs only as good as the strategy behind it.
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2. AI & ML- Python, R
3. Competitive Programming - C++, Java
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5. Game Development - C#, C++
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7. Data Analysis - Python, R
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12. Embedded Systems - C, Assembly
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