Stanley Lam’s Post

The Amazon Catalog team uses generative AI to make product information more useful and A/B testing to evaluate enriched data. Two team members explain how causal random forests and Bayesian structural time series help them extrapolate from sparse A/B data. #Economics #GenerativeAI #ABtesting

Using causal random forests and #Bayesian structural time series to extrapolate from sparse data ensures that customers get the most useful information as soon as possible: https://lnkd.in/e2MkHe2x #GenAI #MachineLearning #AmazonScience

🚀 Here is my first Medium article on the fascinating world of Experiment tracking in Data Science and Machine Learning! 🧪 This article explores the experimental nature of our field, and how to efficiently do countless experiments using a dedicated amazing tool - neptune.ai. If you've ever wondered how to navigate the complex landscape of ML experiments, this article is for you. Whether you're just starting your journey or looking to deepen your understanding, I hope you find valuable insights and inspiration. Feel free to share your thoughts and experiences in the comments. Let's learn and grow together! 🌟 https://lnkd.in/dzNcGS9H #DataScience #MachineLearning #experiment #tracking #AI #mlops

Even though i would prefer LSTM or HCNN, just interesting what people try with foundation models to forecast TS. I do not expect much progress here, since tabular data is not text, but having self attention for the financial time series, would be interesting. Any ideas? #forecasting #ai #ml

🚀 Day 29 of 30 Days of Machine Learning Concepts Challenge Topic: Unveiling the Black Box - Exploring Explainability in Machine Learning! Hey there, LinkedIn fam! 👋 Welcome to Day 29 of our 30 Days of Machine Learning Concepts Challenge. 🔍 Topic Overview: Explainability in machine learning is like having a transparent window into the decision-making process of our models. It involves techniques and methodologies to interpret and understand the inner workings of complex machine learning models, ensuring that their decisions are comprehensible and trustworthy. ✨ Everyday Resemblance: Imagine you're learning a new recipe. Explainability is like understanding why each ingredient is used and how it contributes to the final dish's flavor. Similarly, in machine learning, explainability helps us understand the factors influencing our model's predictions and how different features contribute to the outcomes. 💡 Why Explainability Matters: Explainability matters because it enhances trust, transparency, and accountability in machine learning systems. It allows stakeholders to understand and validate model decisions, identify biases or errors, and ensure compliance with regulations and ethical standards. Additionally, explainable models are easier to debug, interpret, and deploy in real-world applications. Key Concepts in Explainability: 1. Feature Importance: Techniques such as permutation importance, SHAP values, and LIME help identify the most influential features in a model's decision-making process. 2. Model Visualization: Visualizing decision trees, feature interactions, and model internals using techniques like partial dependence plots and model-agnostic visualization methods. 3. Local vs. Global Interpretability: Distinguishing between interpreting individual predictions (local interpretability) and understanding overall model behavior (global interpretability). 4. Model Explanation: Generating human-readable explanations for individual predictions, providing users with insights into why a particular decision was made by the model. 5. Sensitivity Analysis: Assessing the impact of changes in input features on model predictions, helping understand the model's robustness and sensitivity to different inputs. 6. Ethical Considerations: Considering ethical implications, fairness, and biases associated with model predictions, ensuring that machine learning systems uphold ethical standards and avoid harmful outcomes. 📚 Additional Resources: - Interpretability vs Explainability by Amazon (https://lnkd.in/eUyS9QeR) - End-to-End Model Explainability guide by Priyanka Dalmia (Analytics Vidhya)(https://lnkd.in/edYDxMV7) 👉 Up Next (Day 30): Our final day of the challenge 🎉🚀✨

This just in! Anthropic's Claude 3 Opus foundation model is now available on Amazon Bedrock, joining Claude Sonnet and Claude Haiku. Bedrock is now the first and only place to offer all three Claude 3 models in general availability. Claude Opus 3 is the best-performing foundation model (based on multiple industry benchmarks). It is Anthropic's most intelligent model, with best-in-industry performance on highly complex tasks; navigating open-ended prompts and sight-unseen scenarios with almost human-like understanding and fluency. Combined with Bedrock's security, grounding, alignment, and guard rail capabilities - and currently being the only managed service offering all three Claude 3 models; Opus, Sonnet, and Haiku – in GA - there has never been a better time to give generative AI a try. With its exceptional capabilities in understanding and generating human-like responses, Claude 3 Opus empowers businesses to unlock a world of possibilities. From automating intricate processes and conducting sophisticated financial forecasts to accelerating research and development across multiple sectors and sparking innovative ideas through seamless brainstorming sessions. Businesses everywhere can take advantage of these features now. Whether you're seeking to streamline operations, drive cutting-edge innovations, or explore uncharted territories, now is the perfect time to unleash the full potential of Claude 3 Opus on Bedrock. You wil be amazed by the remarkable capabilities of this Anthropic's latest model. https://lnkd.in/ea9ZRdPi

**Machine Learning: A Quick overview of Regression, Classification, Clustering & Association Rules📊** Ever wondered how Spotify predicts your next favorite song?🎵 Or how banks decide to approve or reject loans?💸 It's all about data science and machine learning! 1️⃣ Regression: Predicting a continuous value. - Example: Predicting house prices based on size, location, etc. - Algorithms: Linear Regression, Ridge, Lasso, Decision Trees, SVR. 2️⃣ Classification: Categorizing data into classes. - Example: Identifying if an email is spam or not. - Algorithms: Logistic Regression, K-NN, SVM, Random Forest, Neural Networks. 3️⃣ Clustering: Grouping similar data points without prior labels. - Example: Segmenting customers into groups based on purchasing behavior. - Algorithms: K-means, Hierarchical clustering, DBSCAN. 4️⃣ Association Rules: Discovering relations between variables in datasets. - Example: If you buy bread, you're likely to buy butter (like a buy-one-get-one offer!). - Algorithms: Apriori, Eclat. Dive deeper, and you'll see the beauty of how data can be used to make predictions, classify data, find hidden patterns, and even suggest what you might like next! Let's continue to demystify and harness the power of data science together!🌟 #datascience #machinelearning #AI

Excited to announce that I've completed the "Introduction to Machine Learning" course from Amazon! 🚀 This journey has equipped me with a strong foundation in machine learning concepts, algorithms, and applications. I'm looking forward to leveraging these skills to tackle complex problems and drive innovation in my projects. Huge thanks to Amazon for offering such a comprehensive and insightful course! #MachineLearning #ContinuousLearning #AmazonTraining #TechSkills #DataScience #AI #ProfessionalGrowth

I recently got myself Christopher Bishop's new "Deep Learning: Foundations and Concepts" book. Having read his previous "Pattern Recognition and Machine Learning" book I could not stop looking back a little about how much changed since then (not only that his son went from being aknowledged as family support to a co-author in the new one, which is kind of lovely in one way and makes me reminisce about my own age in another way). I see two big shifts in #machinelearning being clearly displayed in the book: First and foremost, #deeplearning has won the #AI battle for now. There was still a lot of talk about things like support vector machines and decision trees at the time of the first book. All of this has been beaten by sheer layer size and novel "data compression techniques" such as transformers (which themselves have been recently getting hot contenders in the form of new architectures for convolution models, for the people that are following the ongoing hype about #Mamba). From a mathematical perspective I love genetic algorithms and such, but it seems like "big tensor go brrr" is the way to go forward for now. 🤖 Second, where the first book used uncertainty as a tool for exploration and efficiently cramping more and more data into matrixes, the new book focuses on uncertainty as an output itself, i.e. how to generate more and more outputs with the same static matrixes. Video, audio, text, image generation, outputs seem to more and more come in the form of samples taken from more and more complex distributions (and with that I mean we will just be doing x ~ N(0,1) and then say "big tensor go brrr" like we have been doing so far 😏). Really interesting to see where we will be in another 18 years down the road, but I will try to follow up with another 📖 review by then.

🚀 **Day 1 Recap: Unveiling the Power of Data and Predictive Insights in the Amazon Grocery Universe!** Link to the registration form: https://lnkd.in/dAFn4aVm  (also in comment section) Hello LinkedIn Community, The first day of our exhilarating 2-day workshop, "Exploring Amazon Grocery Market Dynamics", has been an incredible journey into the heart of data-driven decision-making and predictive prowess. Here's a glimpse of the remarkable odyssey we embarked upon: 🛒 **Amazon Grocery Data Exploration**: We started by immersing ourselves in the vast Amazon grocery dataset, unraveling its intricacies, and gaining profound insights into the structure and features that define this dynamic market. 🛠️ **Data Preprocessing Mastery**: With precision and dedication, we dived into the world of data preprocessing—cleaning, handling missing values, and transforming features to lay a strong foundation for our analytical journey. 🔮 **Predictive Modeling Magic**: We delved into the world of predictive modeling, applying and fine-tuning a range of supervised machine learning models. From the elegance of Linear Regression to the versatility of Decision Trees and the immense potential of Deep Learning Neural Networks, we explored it all to predict sales and revenue. 📊 **Model Evaluation Adventure**: Understanding the significance of model evaluation, we dissected various evaluation metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared, to assess and validate our models. 🧠 **Deep Learning Introduction**: The day wouldn't have been complete without an introduction to the fascinating realm of deep learning—comprehending neural networks, activation functions, and the marvels of backpropagation. 🌍 **Real-world Applications Unveiled**: We rounded off the day by discussing the practical applications of supervised learning, unveiling how these tools optimize product pricing, predict sales trends, and ultimately transform the Amazon grocery market landscape. Your active participation and engagement made this day an absolute success! The enthusiasm and eagerness to learn were palpable, and I'm excited to see us take this momentum into Day 2. Today, we will dive deeper into Time Series Analysis, Unsupervised Insights, and Generative AI-powered Content Generation. Be ready to unlock more secrets and conquer new horizons in the Amazon grocery realm! Join us today for another day of exploration, innovation, and growth! Best Regards !!!