Artificial Intelligence & ChatGPT Prompts

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Artificial intelligence can change your career by 180 degrees! 📌 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 Face’s 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. 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 ☺️

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How long are coding interviews? The phone screen portion of the coding interview typically lasts up to one hour. The second, more technical part of the interview can take multiple hours. Where can I practice coding? There are many ways to practice coding and prepare for your coding interview. LeetCode provides practice opportunities in more than 14 languages and more than 1,500 sample problems. Applicants can also practice their coding skills and interview prep with HackerRank. How do I know if my coding interview went well? There are a variety of indicators that your coding interview went well. These may include going over the allotted time, being introduced to additional team members, and receiving a quick response to your thank you email.

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Best free resources to learn AI 😻🙌

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Data Analyst Roadmap 💪

Anthropic’s 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 “When Claude incorporates information from the web into its responses, it provides direct citations so you can easily fact check sources.”

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Tips for working with Microsoft Word ✅ To quickly insert the current date, press SHIFT + ALT + D, and to insert the time, SHIFT + ALT + T. ✅ If you accidentally typed a text with the CAPS LOCK key enabled, then select the text in uppercase and press SHIFT + F3. ✅ To speed up the cursor movement through the text, hold down CTRL and use the arrows. ✅ By holding CTRL, you can select all the parts of the text you are interested in, even if they are not arranged in a row. ✅ The F4 key repeats the last command used. ✅ Place the cursor at the beginning of the text selection and click at the end of the desired text segment with the SHIFT key held down for quick selection. ✅ Press CTRL + ENTER to quickly create a new sheet. #lifehack #windows

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Python vs C++ vs Java 😂

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