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👉Ask ChatGPT: What NUMERICAL errors are there in the ML process - Page 1 of 2 ChatGPT 🤖 Numerical errors in the machine learning (ML) process can arise from various sources, affecting the accuracy and performance of models. These errors typically stem from 👉👉limitations in numerical precision, data quality, algorithm design, and computational constraints. Here are some common numerical errors encountered in ML: ### 1. **Floating Point Precision Errors** - **Round-off Errors**: Due to 👉finite precision in floating-point arithmetic, numbers cannot be represented exactly, leading to small errors in computations. These errors 👉accumulate over multiple operations, especially in iterative algorithms like gradient descent. - **Overflow and Underflow**: When numbers exceed the representable range of floating-point numbers, they can cause overflow (resulting in infinity) or underflow (resulting in zero or denormals). ### 2. **Data-Related Errors** -👉 **Imprecise Data Representation**: Converting data types (e.g., from integers to floats) can introduce precision errors, particularly when dealing with very large or very small numbers. - **Missing or Noisy Data**: Missing values, outliers, and noise in the data can lead to 👉inaccurate models. Handling such data requires careful preprocessing and imputation strategies. ### 3. **Algorithmic Errors** - **Convergence Issues**: Numerical stability of optimization algorithms can be problematic. For example, 👉gradient descent might not converge if the 👉learning rate is too high, causing oscillations, or too low, leading to 👉 slow convergence. - **Ill-Conditioned Problems**: Some optimization problems can be ill-conditioned, meaning that small changes in input data can cause large changes in the output. This can lead to instability and unreliable solutions. ### 4. **Matrix Operations** - **Matrix Inversion Errors**: Inverting large matrices or 👉nearly singular matrices (matrices with 👉very small eigenvalues) can lead to significant numerical errors due to 👉👉👉amplification of round-off errors. - **Eigenvalue Computations**: Calculating eigenvalues and eigenvectors of large matrices can be 👉numerically unstable, affecting algorithms like Principal Component Analysis (PCA). ### 5. **Loss Function and Gradient Computations** - **Vanishing and Exploding Gradients**: In deep neural networks, gradients can become 👉extremely small (vanishing gradients) or large (exploding gradients) during backpropagation, leading to slow training or numerical instability. - **Numerical Differentiation**: Approximating derivatives numerically can introduce errors, especially if the step size is not chosen appropriately.

"AI Prompt Engineering" is not a new job. It is an elementary skill. Basic "digital literacy". For at least 20 years, "Office, email and internet" have been mandatory in all CVs. You must now add "Prompt engineering". Prompt engineering is the ability to ask questions and obtain relevant, useful answers or text from GPT (and other LLM/AI tools). I could never list " MS Office" in my CV skills. Not only that I memorized most Office shortcuts, but I've been writing VBScript macros and Java/C++ OLE automation code ever since grad school (thanks Andrei Ionut Damian, Ph.D. I learned so much from you). Listing Office or Internet as a skill would be silly: I can actually write their code. Similarly, I cannot list "prompt engineering" as a skill. I strive to become an AI expert, of course. ChatGpt says that an AI Specialist is a master in machine learning, data science, and artificial intelligence; managing NLP; Python, R, or Java; ML libraries such as TensorFlow, PyTorch, Scikit-Learn. But yes: Prompt engineering is now the basic digital literacy skill, to add to all CVs.

👉Ask ChatGPT: What MATH MODELS are used to predict the need for maintenance of the AIRPLANE frame? 🛬 - PART 2 OF 2 Statistical Analysis: Statistical techniques, such as regression analysis, time-series analysis, and reliability modeling, are used to analyze historical maintenance data, flight data, and inspection records. By identifying patterns, trends, and correlations in the data, statistical models can help predict future maintenance needs, estimate component lifetimes, and optimize maintenance schedules. Machine Learning and Artificial Intelligence: Machine learning algorithms, such as supervised learning, unsupervised learning, and reinforcement learning, can be trained on large datasets of aircraft maintenance and performance data to develop predictive models. These models can learn complex patterns and relationships in the data, enabling more accurate predictions of maintenance needs and failure probabilities. Structural Health Monitoring (SHM): SHM systems use sensors, data acquisition systems, and signal processing techniques to monitor the health and performance of aircraft structures in real-time. Mathematical models are used to analyze sensor data, detect anomalies, and assess the structural integrity of critical components. SHM enables early detection of damage or degradation, allowing for timely maintenance interventions. By combining these mathematical models and techniques, aircraft operators and maintenance providers can optimize maintenance strategies, minimize downtime, and ensure the safety and reliability of aircraft structures throughout their operational life.

ChatGPT Your New Study Partner for Learning Anything, Anytime, Anywhere Maximize your learning ability! Boost your career. Here are top 10 ChatGPT prompts: 📖 Simplify complex information Prompt: Break down (subject/topic) into smaller, easier-to-understand parts. Use analogies and real-life examples to simplify the concept and make it more relatable. 📖 Create a study plan Prompt: I need help to organize my study time for (subject/topic). Can you create a study plan for me, including breaks and practice exercises? 📖 Memorize key information Prompt: What are the most important facts, dates, or formulas related to (topic)? Help me create a memorization technique to remember them easily. 📖 Summarise A YouTube Video Prompt: Summarize the text below into the most important points. Display the points as bullet points with short descriptions. Here is the text: [Paste YT Video Transcription] 📖 Apply your knowledge Prompt: Use your knowledge of (topic) to solve a real-world problem. Explain your thought process and share your solution. 📖 Compare and contrast Prompt: Compare and contrast (concept 1) and (concept 2) to better understand their similarities and differences. Use examples to illustrate your points. 📖 Learn from ‘future’ mistakes Prompt: I made a mistake while practicing (skill). [Describe the mistake]. Can you explain what went wrong and how I can avoid making the same mistake in the future? 📖 Summarize long text Prompt: I need to read a complicated article related to (topic). Here is the content: [insert content]. Can you help me summarize the key points and takeaways from the text? 👉 Follow me Abdelrhman El-sheref for AI tips 🔄 REPOST to help others use AI

And just like that... we have a new model in ChatGPT and two ways to access it: o1-preview and o1-mini. You can still choose 4o if you want! ChatGPT o1 is a new AI model released by OpenAI that represents a significant advancement in artificial intelligence capabilities, particularly in reasoning and problem-solving. Reasoning Abilities ChatGPT o1 is designed to handle more complex tasks through a "chain of thought" approach, similar to human reasoning. It can: Break down problems step-by-step. Explore different strategies to solve problems. Identify and correct mistakes as it works through tasks. Improved Performance Compared to previous models like GPT-4o, ChatGPT o1 shows enhanced capabilities in: Mathematics and coding tasks. Complex, multi-step challenges. Accuracy and reduced "hallucinations" (generating false information). Training and Technology ChatGPT o1 uses a new AI framework called OpenAI o1. It was trained using reinforcement learning, a different approach from previous GPT models. The model can take more time to analyze and deconstruct problems before responding. Availability and Pricing Available to ChatGPT Plus and Team users, with Enterprise and Edu access coming soon. More expensive to use than GPT-4o, with higher token costs for developers (so I think there are limits to how many times we/users can use it). A smaller, more cost-effective version called o1-mini is also available. Limitations Despite its advancements, ChatGPT o1 has some limitations: Struggles with factual knowledge about the world. Lacks capabilities like web browsing or processing files and images.

🎉 Achievement Unlocked: Excel AI Certification! 🤖📊 I’m thrilled to share that I’ve successfully completed the 1-day Excel AI certification workshop conducted by OMs. 🌟 What I Learned: ChatGPT & AI Integration: I explored how to supercharge my Excel tasks by integrating ChatGPT. From complex formulas to data analysis, I’ve harnessed AI to streamline my workflow. Advanced Excel Techniques: Pivot tables, data cleaning, and formula wizardry—these are now my forte! Productivity Boost: AI-driven insights have revolutionized my data preparation and analysis. Why Excel + AI Matters: Work Smarter: Say goodbye to repetitive tasks. AI can handle the heavy lifting, leaving you more time for strategic thinking. Stay Ahead: Excel + AI is the future. Keep learning and adapting to stay competitive. Hands-On Practice: I dove into practical exercises, quizzes, and real-world scenarios. ChatGPT became my trusty sidekick for solving unique Excel challenges. Remember, every cell in an Excel sheet holds potential. Let’s crunch those numbers, automate those tasks, and excel together! 🚀📈👩💼👨💼

Enhancing Data Analysis in ChatGPT: New Features Rolled Out OpenAI has introduced significant improvements to ChatGPT's data analysis capabilities, making it even more powerful and user-friendly. Here’s an overview 👇 Key Enhancements: - Direct File Uploads: You can now upload files directly from Google Drive and Microsoft OneDrive, streamlining the data import process. - Interactive Tables and Charts: Engage with tables and charts in an expandable view, allowing real-time updates and detailed follow-up questions. - Customizable Charts: You can create and customize bar, line, pie, and scatter plot charts and download them for use in presentations or documents. - Enhanced Data Analysis: ChatGPT can handle various data tasks, such as merging datasets, cleaning data, creating charts, and uncovering insights, all by writing and running Python code on your behalf. These features are designed to make data analysis more accessible for beginners and efficient for experts, reducing the time needed for routine tasks and enabling deeper data exploration. For more details on these updates, read the full blog post here: https://lnkd.in/eZnxWCv3 _____________ ✔️ Click "Follow" on the Cohorte page for daily AI engineering news.

🤖Ask ChatGPT: How can we trust ML results when the test data is fixed, and future data may be very different from the models' data? ChatGPT Trusting machine learning (ML) results in real-world applications requires careful consideration of various factors, including the stability and generalization capability of the models. While it's true that ML models are trained on historical data, and future data may differ from the training data, there are several strategies to enhance trustworthiness: Cross-Validation: Use techniques like cross-validation to evaluate model performance on multiple subsets of the data. Cross-validation helps assess the stability and generalization ability of the model across different partitions of the data, providing more robust performance estimates. Time-Series Splitting: If your data is time-dependent, consider using time-series splitting techniques for cross-validation. This approach preserves temporal ordering and mimics real-world scenarios where models are trained on past data and evaluated on future data. <<time series>> Feature Engineering: Develop robust features that capture underlying patterns and relationships in the data. Feature engineering plays a crucial role in -->>improving model generalization by extracting meaningful information from raw data and reducing the risk of overfitting to idiosyncrasies in the training data. Regularization: Apply regularization techniques such as L1 or L2 regularization to prevent overfitting and encourage simpler models. Regularization penalizes complex models, making them less sensitive to noise in the training data and promoting -->>better generalization to unseen data. Ensemble Methods: Utilize ensemble learning techniques, such as bagging, boosting, or stacking, to combine multiple models and leverage their collective predictive power. Ensemble methods often -->> yield more robust and reliable predictions by reducing the impact of individual model biases and errors. Monitoring and Updating: Continuously monitor model performance in production and update models as new data becomes available. Implementing robust monitoring systems allows you to -->>detect drifts in data distribution or model degradation over time, prompting timely model retraining or recalibration. --continues in #2--

👉Ask ChatGPT: what NUMERICAL errors are there in the ML process - Page 2 of 2 ChatGPT 🤖 Numerical errors in the machine learning (ML) process can ... These errors typically stem from limitations in numerical precision, data quality, algorithm design, and computational constraints. --continued below-- ### 6. **Regularization and Penalty Terms** - **Regularization Parameters**: Choosing inappropriate regularization parameters can lead to 👉overfitting or underfitting. Numerical errors can arise if the parameters are too small (leading to negligible effect) or too large (dominating the objective function). ### 7. **Sampling and Stochastic Methods** - **Monte Carlo Methods**: In methods like Monte Carlo simulations or stochastic gradient descent, random sampling introduces variability and potential errors. The accuracy of these methods depends on the 👉number of samples and their quality. - **Batch Size in Stochastic Gradient Descent**: The choice of 👉 batch size affects the variance of the gradient estimates. Smaller batch sizes lead to noisier gradients, while larger batch sizes can smooth out the noise but require more computation. ### 8. **Computational Errors** - **Parallel and Distributed Computing**: When training models on distributed systems, synchronization issues, communication delays, and 👉👉inconsistencies in floating-point operations across different hardware can introduce 👉numerical errors. - **Hardware Limitations**: The choice of hardware (CPU vs. GPU) and the precision used (e.g., single vs. double precision) can impact numerical accuracy. 👉👉GPUs often use single precision for speed, which can lead to precision loss compared to 👉double precision. ### 👉👉👉Mitigation Strategies To minimize numerical errors in the ML process, consider the following strategies: - **Use Stable Algorithms**: Prefer numerically stable algorithms and formulations, such as avoiding explicit matrix inversion by using factorizations (e.g., LU, QR). - 👉👉**Precision Management**: Choose appropriate precision levels (single vs. double) based on the problem requirements and computational resources. - **Normalization and Standardization**: Normalize or standardize input data to 👉reduce the risk of numerical issues. - **Regularization**: Apply regularization techniques to mitigate overfitting and improve 👉 numerical stability. - **Gradient Clipping**: Implement gradient clipping to handle vanishing and exploding gradients in deep learning. - **Batch Size Optimization**: Experiment with different batch sizes in stochastic methods to balance noise and computational efficiency. - **Robust Preprocessing**: Handle 👉missing values, outliers, and noisy data appropriately through preprocessing techniques. By being aware of these potential numerical errors and adopting suitable strategies, the robustness and reliability of machine learning models can be significantly improved.

ChatGPT is an amazing tool. But it can't do everything. Here are 7 AI websites to save a ton of time and work: 1. Codedesign AI / CodeDesign.ai This tool allows you to create stunning websites without ever touching a line of code. With Codedesign AI, you can build: ● Landing pages ● Websites ● Sales Funnels ● Portfolio 𝗧𝗿𝘆 𝗶𝘁: https://codedesign.ai 2. Abacus AI / Abacus.AI The ultimate AI assistant for you. Access top LLMs like GPT-4o, Claude, Llama-3, and more, all in one spot. 𝗧𝗿𝘆 𝗶𝘁: https://lnkd.in/gPWhFRe7 3. Powerdrill AI / Powerdrill Your Personal AI Data Analyst Experience ChatGPT with the powers of a Human Data Analyst… 𝗧𝗿𝘆 𝗶𝘁: https://lnkd.in/d2ew3syt 4. Rask AI / Rask AI Best AI tool for Audio and Video Translation You can use it for video, audio, and content creation. 𝗧𝗿𝘆 𝗶𝘁: https://lnkd.in/dxZyCXCC 5. Momen Create Web Apps with ZERO Coding. Craft your app from frontend to backend with a simple drag-and-drop editor. 𝗧𝗿𝘆 𝗶𝘁: https://lnkd.in/dqjBtAyd 6. Julius AI / Julius AI Save time. Make Julius do your data work. Turn hours of Excel into minutes on Julius. 𝗧𝗿𝘆 𝗶𝘁: https://julius.ai 7. AI Pal Your personal writing assistant - Ask questions to GPT-3.5, 4, Claude or Gemini - Reading & summarizing web pages - Auto spell check & paraphrasing - Real-time translation 𝗧𝗿𝘆 𝗶𝘁: https://bit.ly/3XoFkGj Happy Learning! 💜 That's a wrap! If you enjoyed this post or found it useful: 1. Follow me Aakash K. for more of these. 2. Repost to share this post with your network.