Artificial Intelligence & ChatGPT Prompts

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🖥 Git: Merging vs Rebasing In git, when there are some changes in a parent branch from which you forked, there are two strategies to incorporate them into your working branch: ✨ Merging: As the name suggests, 'merges' the parent branch into your branch. The advantage is that handling conflicts (if any) is easier, since you only need to resolve them for the merge commit once. However, this may make the git history a bit harder to follow ✨ Rebasing: Takes all the commits you made in your branch, and applies them on top of the head of the parent branch. As if repositioning the base of the branch. This helps to create a linear and easy to follow git history, but conflict resolution may be tedious and you need to force push the branch to the origin.

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Don't take life as a problem Life is a mystery to be lived, not a problem to be solved. Live in this mystery, dance, sing, enjoy - but don't try to "solve" it as if it were a problem. Life invites you to experience it and admire it. She wants you to become like a child. Learn to enjoy life, learn to perceive it as a game. Everything should be perceived as a game - even death. TrueMinds

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Microsoft releases PCs ‘designed for AI, going to be completely new experience! It will be called "Copilot+ PC”, which uses chips made by Qualcomm rather than Intel, and will have a battery life of 22 hours, Microsoft said, which is slightly ahead of what Apple delivers with its MacBook Pro and MacBook Air. Their new feature called RECALL is going to be very exciting. https://blogs.microsoft.com/blog/2024/05/20/introducing-copilot-pcs/

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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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