a• AI Summer– How Attention works in Deep Learning: understanding the attention mechanism in sequence models– How diffusion models work: the math from scratch– Understanding Maximum Likelihood Estimation in Supervised Learning• Assembly AI– Introduction to Diffusion Models for Machine Learning• Jay Alammar– The Illustrated Stable Diffusion• Educative.io– Grokking Machine Learning Interview– Web Application and Software Architecture 101• Udemy– AWS Certified Machine Learning Specialty 2023 - Hands On!• Databricks Academy– List of courses– Optimizing Apache Spark on Databricks– Developer tools and guidance– CI/CD with Jenkins on Databricks– Databricks Data Science & Engineering guide– Databricks Machine Learning guide– Optimization recommendations on Databricks– Data Warehousing: What is Databricks SQL?– What is Delta Lake?– Databricks integrations– Data governance guide– Load data into the Databricks Lakehouse– Exploratory data analysis on Databricks: Tools and techniques– Introduction to data preparation in Databricks– Share data securely using Delta Sharing– Get started articles, tutorials, and best practices– Unit testing for notebooks• Neptune.ai– ML Model Registry: What It Is, Why It Matters, How to Implement It– Dimensionality Reduction for Machine Learning– K-Means Clustering Explained– Knowledge Distillation: Principles, Algorithms, Applications– When to Choose CatBoost Over XGBoost or LightGBM [Practical Guide]– Distributed Training: Guide for Data Scientists– A Comprehensive Guide to Ensemble Learning: What Exactly Do You Need to Know– Gradient Boosted Decision Trees [Guide]: a Conceptual Explanation– Recommender Systems: Lessons From Building and Deployment– How to Deal With Imbalanced Classification and Regression Data– ARIMA vs Prophet vs LSTM for Time Series Prediction– MLOps at a Reasonable Scale [The Ultimate Guide]– Model Debugging Strategies: Machine Learning Guide– Model Deployment Challenges: 6 Lessons From 6 ML Engineers– Self-Supervised Learning and Its Applications– Vanishing and Exploding Gradients in Neural Network Models: Debugging, Monitoring, and Fixing– How to Select a Model For Your Time Series Prediction Task [Guide]– Hugging Face Pre-trained Models: Find the Best One for Your Task– 9 Steps of Debugging Deep Learning Model Training– Deploying ML Models: How to Make Sure the New Model Is Better Than the One in Production? [Practical Guide]– 7 Cross-Validation Mistakes That Can Cost You a Lot [Best Practices in ML]– Tabular Data Binary Classification: All Tips and Tricks from 5 Kaggle Competitions– How to Version Control Data in ML for Various Data Sources– Model Monitoring for Time Series– Building a Sentiment Classification System With BERT Embeddings: Lessons Learned– Training Models on Streaming Data [Practical Guide]– Building Visual Search Engines with Kuba Cieślik– ML Collaboration: Best Practices From 4 ML Teams– Optimizing Models for Deployment and Inference– Why is Git Not the Best for ML Model Version Control– Classification in ML: Lessons Learned From Building and Deploying a Large-Scale Model• Google– All BigQuery ML tutorials– Neural machine translation with a Transformer and Keras– A Comprehensive Study Guide for the Google Professional Machine Learning Engineer Certification– Machine Learning Engineer Learning Path– Google Cloud Learning Paths– Professional Machine Learning Engineer– Machine Learning - Advanced Courses– ML Guides - Good Data Analytics– Rules of ML– People + AI Guidebook• AlgoExpert– MLExpert (CNN, RNN, Naive Bayes Optimization, K-Means)– Large-Scale Machine Learning (Deep Learning Models, Model Validation)– System Design Fundamentals– ML Design Questions• HuggingFace– Accelerating Document AI– Probabilistic Time Series Forecasting with 🤗 Transformers• Investopedia– Explaining the World Through Macroeconomic Analysis– Shapley Value– The Prisoner’s Dilemma in Business and the Economy– R-Squared Formula, Regression, and Interpretations– Analysis of Variance (ANOVA) Explanation, Formula, and Applications– Degrees of Freedom in Statistics Explained: Formula and Example• Courses– Theory of Statistics II, Stats 300B [Stanford]– Statistics 200: Introduction to Statistical Inference [Stanford]– NLP Course• Scale AI– Guides* Data Labeling* Diffusion Models* Training and Building ML Models* AI for E-Commerce* Computer Vision• TutorialsPoint– pytest• Other– Let's build GPT: from scratch, in code, spelled out (YouTube Video from Andrei Karpathy)– Learn PyTorch for Deep Learning: Zero to Mastery book– Organize Python code like a PRO– Regression Analysis: How Do I Interpret R-squared and Assess the Goodness-of-Fit?– A Guide to the Regression of Rates and Proportions– Machine Learning Students Overfit to Overfitting– Who Owns the Generative AI Platform?– HOW TO OPERATE APACHE AIRFLOW WITH GITLAB CI/CD