Prompt Discovery


Learn prompt discovery to uncover the most effective prompts and combinations thereof to achieve specific tasks, while also considering factors like response quality, model performance, and computational efficiency

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Techniques to Boost RAG Performance in Production


This article discusses several advanced techniques that can be applied at different stages of the RAG pipeline to enhance its performance in a production setting.

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Understanding the Differences in Language Models - Transformers vs. Markov Models


This article explores distinguishing details of Markov Models and Transformer-based models like GPT, focusing on how they predict the next character in a sequence. It explores the fundamental differences between these models, with a particular emphasis on how the self-attention mechanism in Transformer models makes a difference compared to the fixed context length in Markov models.

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Problems with Langchain and how to minimize their impact


Beyond the Hype - LangChain's Hidden Flaws and How to Master AI Development.

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Harnessing the Power of Dependency Injection for Improved Testability in Python


Learn how to use dependency injection to decouple dependencies from our functions, methods, or classes, making it easier to test and maintain our code.

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The Role and Responsibilities of a Forward Deployed Engineer - Bridging the Gap Between Software Products and Customer Needs


Bridging the gap between software products and customer needs, Forward Deployed Engineers are the game-changers of enterprise software. Discover their unique role in driving success and why it's in high demand. Don't miss out!

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The Best Vector Databases for Storing Embeddings


Delve into the World of Vector Databases Fueling NLP's Transformative Journey.

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Attacking Differential Privacy Using the Correlation Between the Features


Learn how the differential privacy works by simulating attack on data protected with that technique.

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Are LIME Explanations Any Useful?


Don't let black box models hold you back. With LIME, you can interpret the predictions of even the most complex machine learning models.

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Intrinsic vs. Extrinsic Evaluation - What's the Best Way to Measure Embedding Quality?


Learn how to measure the quality of word and sentence embeddings in natural language processing (NLP), including intrinsic and extrinsic evaluation, and their strengths and limitations.

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LIME - Understanding How This Method for Explainable AI Works


Discover how the LIME method can help you understand the important factors behind your model's predictions in a simple, intuitive way.

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SHAP - Understanding How This Method for Explainable AI Works


Discover how the SHAP method can help you understand the important factors behind your model's predictions in a simple, intuitive way.

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LIME Tutorial


Unveiling the mysteries of AI decisions? Let us dive into LIME, the tool that sheds light on the black box.

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Rethinking the Link Between Speech and Expertise


We often associate eloquent speech with intelligence and knowledge. But what if I told you that this assumption is not always true?

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Understanding AI with ELI5 - Demystifying Decisions (tutorial)


Want to know why your AI model made that decision? ELI5 has got you covered. Let's dive into Explainable AI with ELI5.

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"Comprehensive Guide to Interpreting R\xB2, MSE, and RMSE for Regression Models."


Don't let misleading metrics fool you. Master the art of analyzing regression model performance and make smarter decisions.

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Beyond Airflow - 10 Workflow Tools You Need to Know


Looking for a new workflow management tool? Do not settle for Apache Airflow just because it is popular. Discover 10 cutting-edge alternatives that could be a better fit for your needs.

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Is the the Game Theory Any Useful for Data Science?


Exploring the intersection of game theory and data science - insights into decision-making, network behavior, and optimization algorithms.

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The Impact of Search Engines and AI Generative Models on Mental and Cognitive Capabilities


Understand the effects of search engines and AI on our mental and cognitive capabilities. Equip yourself with the knowledge you need to make informed decisions about your own usage of these technologies.

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Becoming a Data Wizard - The Benefits of Learning Databricks


Learn how Databricks can help you master big data, improve data processing and machine learning skills and excel in your career. Boost your career with this powerful platform.

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Common Types of Data Science Projects


Learn about common types of data science projects and best practices for approaching them. From end-to-end individual work to production-ready projects, this guide covers it all.

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How to Detect ChatGPT-Generated Text?


Discover the latest methods for distinguishing machine-generated text from the human-written text. Learn about statistical, syntactic, semantic, and neural network-based approaches. Stay up-to-date with the latest research in NLP and AI.

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Maximizing Efficiency in MLOps - How Blue/Green Deployment Can Help?


Learn about blue/green deployment in MLOps, its usefulness and when to use it, and the cost and complexity of maintaining two separate environments

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MLOps Certifications - A Comprehensive Guide


Learn about popular MLOps certifications offered by Amazon, Google, and Microsoft. Understand if they are worth the effort and money and discover free MLOps certifications available to gain knowledge and understanding of the field.

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Roles in MLOps


Learn about the different roles in MLOps and the responsibilities of each role, including Model Deployment Engineer, Data pipeline Engineer, Model Monitoring Engineer, Model Governance Engineer, Machine Learning Infra Engineer and Machine Learning Platform Engineer.

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50 Questions for MLOps Engineer Job Interview


Get ready for your next MLOps Engineer interview with our comprehensive list of 50+ questions. Covering topics like deployment, management, data pipeline, monitoring, and more.

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MLOps Roles of the Future


Discover the future of MLOps specializations, including Explainable AI/MLOps, Federated Learning/Edge MLOps, Reinforcement Learning/MLOps, AI/ML in IoT and IIoT, Model Explainability and Fairness.

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Is MLOps a Good Career?


Is a career in MLOps right for you? Learn about the pros and cons of this growing field, including high demand, high earning potential, exciting work, and career growth opportunities.

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Discovering Hidden Gems - Popular and Lesser-Known Dataset Sharing Platforms


"Looking for the key to unlocking valuable datasets? Dive into the world of Kaggle, UCI, and more as we unveil the best platforms for data enthusiasts."

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Top 10 Python Libraries for Document Classification


Unlock the power of document classification with these top Python libraries! Discover the best tools for effortless text analysis and more.

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Pro Tips for Diagnosing Regression Model Errors


Improve your regression model's accuracy and predictability by uncovering hidden errors with these essential plots.

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Unleashing the Power of T-Sne for Dimensionality Reduction in Python


Want to create beautiful visualizations from complex data? Discover the power of T-SNE for dimensionality reduction in Python.

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Finding Errors in Data - Data Validation


Explore methods to detect & fix errors in data, including validation, visualizations, statistical tests, cleaning techniques, machine learning & data quality tools. Get concise, easy to understand information with examples & links to external resources.

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Evaluation of Interpretability for Explainable AI


Learn about the evaluation of interpretability in machine learning with this guide. Discover different levels and methods for assessing the explainability of models.

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Metrics Used to Compare Histograms


Learn about metrics used to compare histograms with examples of how to calculate them in python. From Chi-Squared distance to Kullback-Leibler divergence and Earth Mover's distance. A comprehensive guide.

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Data Science Command-Line Tools


Description of GNU utils and other less standard tools that helps with processing data from CLI or with shell scripts.

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Learn Bayesian Methods in 4 Steps - By Reading and by Doing


This post proposes a 4-step path for learning Bayesian methods. The first step is going through the book "Bayesian methods for hackers", second, using complementary books for probability and statistics, the third, reading How to become a Bayesian in eight easy steps, and last, going through the book full of exercises - "Think Bayes".

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Kaggle Evaluation Metrics Used for Regression Problems


"This post describe evaluation metrics used in Kaggle competitions where problem to solve is has regression nature. Eight different metrics are described, namely - Absolute Error (AE), Mean Absolute Error (MAE), Weighted Mean Absolute Error (WMAE), Pearson Correlation Coefficient, Spearman\u2019s Rank Correlation, Root Mean Squared Error (RMSE), Root Mean Squared Logarithmic Error (RMSLE), Mean Columnwise Root Mean Squared Error (MCRMSE)."

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How to Install TensorFlow and Keras on Windows 10


Guide on how to install TensorFlow cpu-only version - the case for machines without GPU supporting CUDA. Step-by-step procedure starting from creating conda environment till testing if TensorFlow and Keras Works.

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Darwin Approach to Traveling Salesman


Can the evolutionary approach crash the problem that brute-forcing will last far more than the age of the universe? This post shows how to attack the Traveling Salesman Problem using Darwin's approach. I'm describing the evolution model and design decisions. See the animation of how the population was evolving through the epochs.

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How to Organize Data Science Project Based on Jupyter Notebook


Having several notebook-based projects behind you might result in a mess in the projects directory. Organize your Data Science project based on Jupyter notebooks in a way that one can navigate through it. Especially that "the one" will be most probably you in a few months time. To achieve that, keep your projects directory clean, name the project in a descriptive way and take care of the internal structure of the project.

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What's Cooking


Exploratory Data Analysis of the Kaggle's "What's cooking" competition dataset to get understanding what kind of data we are dealing with and get intuition of existing dependencies.

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