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FileDate: 2025-02-28 | ProcTime: 2025-02-28 13:16:10 | Count: 482

MSCE (sorted by timestamp)

• 2025-02-27 09:53:13 | confident-ai/deepeval: The LLM Evaluation Framework | MSCE>ML

• 2025-02-23 10:46:34 | Deep Dive into LLMs like ChatGPT - YouTube | MSCE>ML

• 2025-02-20 20:10:10 | Step-by-Step Diffusion: An Elementary Tutorial | MSCE>ML

• 2025-02-20 14:06:48 | microsoft/ai-agents-for-beginners: 10 Lessons to Get Started Building AI Agents | MSCE>ML

• 2025-02-13 17:17:13 | workofart/ml-by-hand: A deep learning library built from scratch with complex neural networks examples built on top for learning purposes. | MSCE>ML

• 2025-02-13 01:17:45 | Interactive Linear Algebra | MSCE>Math

• 2025-02-10 19:22:11 | (WIP) A Little Bit of Reinforcement Learning from Human Feedback | MSCE>ML

• 2025-01-16 10:49:41 | prakhar1989/awesome-courses: :books: List of awesome university courses for learning Computer Science! | MSCE>CS

• 2025-01-12 16:11:37 | Coding with LLMs | MSCE>ML

• 2025-01-06 07:12:37 | Neural networks - YouTube | MSCE>ML

• 2024-12-31 20:48:53 | aie-book/resources.md at main · chiphuyen/aie-book | MSCE>ML

• 2024-12-25 22:41:04 | GitHub - chiphuyen/aie-book: [WIP] Resources for AI engineers. Also contains supporting materials for the book AI Engineering (Chip Huyen, 2025) | MSCE>ML

• 2024-12-08 17:51:04 | 5-Day Gen AI Intensive Course with Google Learn Guide | Kaggle | MSCE>ML

• 2024-11-09 23:48:03 | Transformers from scratch | peterbloem.nl | MSCE>ML

• 2024-11-06 12:43:39 | LLM Evaluations: A Complete Course | MSCE>ML

• 2024-11-01 08:00:23 | Efficient Machine Learning with R | MSCE>ML

• 2024-10-24 18:52:11 | GitHub - srush/Autodiff-Puzzles | MSCE>ML

• 2024-10-16 17:53:33 | NannyML/The-Little-Book-of-ML-Metrics: The book every data scientist needs on their desk. | MSCE>ML

• 2024-10-13 11:51:09 | NannyML/The-Little-Book-of-ML-Metrics: The book every data scientist needs on their desk. | MSCE>ML

• 2024-10-13 10:18:28 | cosmic-cortex/mlfz: An educational machine learning library. | MSCE>ML

• 2024-09-30 11:28:48 | srush/GPU-Puzzles: Solve puzzles. Learn CUDA. | MSCE>ML

• 2024-09-27 16:12:38 | Applied Machine Learning in Python: a Hands-on Guide with Code — Applied Machine Learning in Python | MSCE>ML

• 2024-09-24 14:54:00 | Diffusion Explainer: Stable Diffusion Explained with Visualization | MSCE>ML

• 2024-09-24 14:52:08 | andresvourakis/data-scientist-handbook: This is a repo with links to everything you'd ever want to learn about data science | MSCE>ML

• 2024-09-23 12:51:27 | The Pragmatic Programmer for Machine Learning | MSCE>ML

• 2024-09-19 16:44:34 | An In-Depth Guide to Contrastive Learning: Techniques, Models, and Applications | MSCE>ML

• 2024-09-15 18:10:31 | The Tensor Cookbook | MSCE>ML

• 2024-09-15 18:04:05 | [2403.18103] Tutorial on Diffusion Models for Imaging and Vision | MSCE>ML

• 2024-09-15 13:14:05 | Machine Learning Engineering Open Book | MSCE>ML

• 2024-09-04 12:55:58 | einatboro/awesome-survival-analysis: Resources for Survival Analysis | MSCE>Stat

• 2024-09-04 10:28:56 | Diffusion is spectral autoregression – Sander Dieleman | MSCE>Engg

• 2024-09-04 00:13:52 | DorsaRoh/Machine-Learning: Machine learning from scratch | MSCE>ML

• 2024-08-31 08:39:23 | rasbt/LLM-workshop-2024: A 4-hour coding workshop to understand how LLMs are implemented and used | MSCE>ML

• 2024-08-29 08:02:13 | NirDiamant/RAG_Techniques: This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and contextually rich responses. | MSCE>ML

• 2024-08-18 23:25:47 | Causal Inference in R | MSCE>Stat

• 2024-08-18 23:24:18 | From the Circle to Epicycles (Part 1) - An animated introduction to Fourier Series | andreinc | MSCE>Engg

• 2024-08-18 16:12:55 | Seeing Theory: Visual intro to probability and statistics | MSCE>Stat

• 2024-08-18 00:54:16 | Physics-based Deep Learning | MSCE>ML

• 2024-08-17 13:54:14 | Active Statistics | MSCE>Stat

• 2024-08-06 22:31:55 | Linear Algebra Done Right | MSCE>Math

• 2024-07-28 10:25:52 | Elements of Data Science | MSCE>Stat

• 2024-07-28 10:21:54 | A User’s Guide to Statistical Inference and Regression | MSCE>Stat

• 2024-06-30 08:08:41 | visenger/awesome-mlops: A curated list of references for MLOps | MSCE>ML

• 2024-06-10 23:43:15 | Shubhamsaboo/awesome-llm-apps: Collection of awesome LLM apps with RAG using OpenAI, Anthropic, Gemini and opensource models. | MSCE>ML

• 2024-06-10 23:26:25 | Applied LLMs - What We’ve Learned From A Year of Building with LLMs | MSCE>ML

• 2024-06-10 17:23:03 | GenAI Handbook | MSCE>ML

• 2024-06-06 01:01:51 | Signal Processing for Hearing – Lecture Series – Australian Hearing Hub | MSCE>Engg

• 2024-05-21 17:52:57 | naklecha/llama3-from-scratch: llama3 implementation one matrix multiplication at a time | MSCE>ML

• 2024-05-12 11:59:01 | moment-timeseries-foundation-model.github.io | MSCE>ML

• 2024-05-09 11:54:13 | How LLMs Work, Explained Without Math - miguelgrinberg.com | MSCE>ML

• 2024-05-07 23:26:13 | Machine Unlearning in 2024 - Ken Ziyu Liu - Stanford Computer Science | MSCE>ML

• 2024-05-07 21:37:49 | Book: Alice’s Adventures in a differentiable wonderland - Simone Scardapane | MSCE>ML

• 2024-04-26 02:27:27 | web.mit.edu/dimitrib/www/RLbook.html | MSCE>ML

• 2024-04-24 00:29:57 | Generative AI for Beginners | MSCE>ML

• 2024-04-23 09:18:35 | Creating a Transformer From Scratch - Part Two: The Rest of the Transformer | Mixed Precision | MSCE>ML

• 2024-04-23 09:18:16 | Creating a Transformer From Scratch - Part One: The Attention Mechanism | Mixed Precision | MSCE>ML

• 2024-04-23 07:56:06 | A Visual Guide to Vision Transformers | MDTURP | MSCE>ML

• 2024-04-21 08:12:41 | Math Matters Problems – Department of Mathematics | MSCE>Math

• 2024-04-17 22:07:21 | Machine Learning for Engineers: Book List 2024 — Bill de hÓra | MSCE>ML

• 2024-04-09 22:42:12 | Models Demystified | MSCE>ML

• 2024-04-09 22:41:06 | The Atlas for the Aspiring Network Scientist | MSCE>ML

• 2024-04-09 22:37:56 | Visualizing Attention, a Transformer's Heart | Chapter 6, Deep Learning | MSCE>ML

• 2024-04-04 22:58:35 | CausalML Book | MSCE>ML

• 2024-04-04 22:56:50 | A Course in Exploratory Data Analysis | MSCE>ML

• 2024-03-26 15:01:08 | Lessons in Statistical Thinking | MSCE>Stat

• 2024-03-19 08:18:00 | Applied Machine Learning for Tabular Data | MSCE>ML

• 2024-03-16 08:46:54 | CausalML Book | MSCE>ML

• 2024-03-12 04:24:37 | Regression and Other Stories | MSCE>Stat

• 2024-02-27 14:27:28 | Feature Engineering A-Z | MSCE>ML

• 2024-02-13 08:04:01 | time-series-foundation-models | MSCE>ML

• 2024-02-12 16:35:39 | Stanford CS25 - Transformers United - YouTube | MSCE>ML

• 2024-02-07 18:40:42 | Supervised Machine Learning for Science | MSCE>ML

• 2024-01-31 10:12:06 | rasbt/LLMs-from-scratch: Implementing a ChatGPT-like LLM from scratch, step by step | MSCE>ML

• 2024-01-31 00:04:17 | stas00/ml-engineering: Machine Learning Engineering Open Book | MSCE>ML

• 2024-01-25 10:21:23 | State Space Models: A Modern Approach | MSCE>Engg

• 2024-01-23 10:30:38 | DataTalksClub/data-engineering-zoomcamp: Free Data Engineering course! | MSCE>ML

• 2024-01-23 10:15:39 | Deep Learning Fundamentals - Lightning AI | MSCE>ML

• 2024-01-22 08:52:01 | Math Academy | MSCE>Math

• 2024-01-09 19:31:29 | rmcelreath/stat_rethinking_2024 | MSCE>Stat

• 2024-01-02 12:30:35 | Machine Learning School | MSCE>ML

• 2024-01-02 06:36:13 | GitHub - mlabonne/llm-course: Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks. | MSCE>ML

• 2023-12-25 10:09:21 | Think Bayes 2 — Think Bayes | MSCE>Stat

• 2023-12-17 11:25:15 | Deep Learning - Foundations and Concepts | MSCE>ML

• 2023-12-15 10:03:14 | krishnaik06/Roadmap-To-Learn-Generative-AI-In-2024 | MSCE>ML

• 2023-11-23 17:23:19 | Generative AI for Beginners | MSCE>ML

• 2023-11-06 10:02:53 | Giskard-AI/giskard: 🐢 The testing framework for ML models, from tabular to LLMs | MSCE>ML

• 2023-11-03 14:00:25 | Stochastic Processes in Cell Biology: Volume II | SpringerLink | MSCE>Bio

• 2023-11-02 11:59:15 | Introduction to Probability for Computing | MSCE>CS

• 2023-10-28 11:32:29 | Getting started with Llama 2 - AI at Meta | MSCE>ML

• 2023-10-24 21:19:39 | Deep Learning course | MSCE>ML

• 2023-10-21 13:44:31 | Software Design by Example | MSCE>CS

• 2023-10-20 07:46:50 | autodiff_puzzlers.ipynb - Colaboratory | MSCE>ML

• 2023-10-15 15:34:21 | hkproj/pytorch-stable-diffusion: Stable Diffusion implemented from scratch in PyTorch | MSCE>ML

• 2023-10-15 15:33:00 | Introduction to Modern Statistics (2nd Ed) | MSCE>Stat

• 2023-10-14 14:26:32 | Autodiff-Puzzles | MSCE>ML

• 2023-09-25 15:17:59 | Ghassen-Chaabouni/machine_learning_games: Set of games and simulations designed to experiment with QLearning, Neuroevolution, and PoseNet. | MSCE>ML

• 2023-09-24 17:17:04 | Efficient deep learning | MSCE>ML

• 2023-09-07 16:48:57 | Python Numerical Methods: A Guide For Engineers And Scientists | MSCE>CS

• 2023-09-05 23:26:33 | rougier/ML-Recipes: A collection of stand-alone Python machine learning recipes | MSCE>ML

• 2023-08-24 22:28:15 | Resources for Learning Measure Theory – Brett Mullins – Researcher - Data Scientist | MSCE>Math

• 2023-08-24 12:58:05 | Correlation Matrix Stress Testing: Random Perturbations of a Correlation Matrix | Portfolio Optimizer | MSCE>Stat

• 2023-08-19 05:11:14 | Catching up on the weird world of LLMs | MSCE>ML

• 2023-08-18 15:12:12 | Comparison of Sorting Algorithms - CodersLegacy | MSCE>CS

• 2023-08-18 14:49:14 | Harvard CS50’s Artificial Intelligence with Python – Full University Course - YouTube | MSCE>ML

• 2023-08-14 14:10:30 | Peter Shor - 8.370/18.435 Lecture Notes 2022 | MSCE>CS

• 2023-08-11 16:36:35 | Llama from scratch (or how to implement a paper without crying) | Brian Kitano | MSCE>ML

• 2023-08-11 08:40:35 | A non-mathematical introduction to Kalman Filters for programmers - Pravesh Koirala | MSCE>Engg

• 2023-08-10 00:07:23 | Large language models, explained with a minimum of math and jargon | MSCE>ML

• 2023-07-26 07:04:01 | srush/LLM-Training-Puzzles: What would you do with 1000 H100s... | MSCE>ML

• 2023-07-19 10:49:45 | What AI can do with a toolbox... Getting started with Code Interpreter | MSCE>ML

• 2023-06-17 14:30:25 | Bayesian Statistical Methods | MSCE>Stat

• 2023-06-05 10:34:44 | Generative AI learning path | Google Cloud Skills Boost | MSCE>ML

• 2023-05-28 20:55:41 | AI Canon | Andreessen Horowitz | MSCE>ML

• 2023-05-19 10:29:34 | llm, ttok and strip-tags—CLI tools for working with ChatGPT and other LLMs | MSCE>ML

• 2023-05-19 09:20:11 | nomic-ai/gpt4all: gpt4all: an ecosystem of open-source chatbots trained on a massive collections of clean assistant data including code, stories and dialogue | MSCE>ML

• 2023-05-17 09:49:00 | GT-RIPL/Awesome-LLM-Robotics: A comprehensive list of papers using large language/multi-modal models for Robotics/RL, including papers, codes, and related websites | MSCE>ML

• 2023-05-17 09:48:30 | Everything-LLMs-And-Robotics: The world's largest GitHub Repository for LLMs + Robotics | MSCE>ML

• 2023-05-03 06:05:49 | harryzhangOG/Deep-RL-Notes: A collection of comprehensive notes on Deep Reinforcement Learning, customized for UC Berkeley's CS 285 (prev. CS 294-112) | MSCE>ML

• 2023-05-01 12:42:12 | LangChain NLP Course - YouTube | MSCE>ML

• 2023-04-23 12:49:02 | MIT course: FUTURE OF AI | MSCE>ML

• 2023-04-09 15:13:51 | fast.ai - From Deep Learning Foundations to Stable Diffusion | MSCE>ML

• 2023-03-26 10:44:39 | Machine Learning  |  Google Developers | MSCE>ML

• 2023-03-25 16:50:10 | promptslab/Awesome-Prompt-Engineering: This repository contains a hand-curated resources for Prompt Engineering with a focus on Generative Pre-trained Transformer (GPT), ChatGPT, PaLM etc | MSCE>ML

• 2023-03-25 12:56:10 | modelscope/modelscope: ModelScope: bring the notion of Model-as-a-Service to life. | MSCE>ML

• 2023-03-17 17:16:51 | Applied Machine Learning | Applied Machine Learning | MSCE>ML

• 2023-03-17 12:58:19 | Prompt Engineering Guide | Prompt Engineering Guide | MSCE>ML

• 2023-03-15 14:22:07 | GitHub - operatorai/modelstore: 🏬 modelstore is a Python library that allows you to version, export, and save a machine learning model to your filesystem or a cloud storage provider. | MSCE>ML

• 2023-03-11 00:28:11 | Neural Network from Scratch (No NumPy) | by Piotr Lachert | Towards Data Science | MSCE>ML

• 2023-03-07 14:48:17 | jaymody/picoGPT: An unnecessarily tiny implementation of GPT-2 in NumPy. | MSCE>ML

• 2023-03-06 09:33:40 | rasbt/machine-learning-notes: Collection of useful machine learning codes and snippets (originally intended for my personal use) | MSCE>ML

• 2023-03-04 00:21:59 | [2302.07730] Transformer models: an introduction and catalog | MSCE>ML

• 2023-03-04 00:20:46 | Understanding Large Language Models -- A Transformative Reading List | MSCE>ML

• 2023-02-26 15:14:47 | Introduction to Data-Centric AI | MSCE>ML

• 2023-02-17 22:20:14 | GPT in 60 Lines of NumPy | Jay Mody | MSCE>ML

• 2023-02-12 09:19:27 | Understanding and Coding the Self-Attention Mechanism of Large Language Models From Scratch | MSCE>ML

• 2023-02-06 14:07:51 | dair-ai/Prompt-Engineering-Guide: Guide and resources for prompt engineering | MSCE>ML

• 2023-01-29 10:39:48 | The Illustrated Machine Learning Website | MSCE>ML

• 2023-01-25 19:32:35 | Datasets for Machine Learning and Deep Learning | MSCE>ML

• 2023-01-24 09:55:33 | https://udlbook.github.io/udlbook/ | MSCE>ML

• 2023-01-22 11:24:47 | Transformer models: an introduction and catalog — 2023 Edition - AI, software, tech, and people, not in that order… by X | MSCE>ML

• 2023-01-15 09:47:50 | dair-ai/ML-Papers-Explained: Explanation to key concepts in ML | MSCE>ML

• 2023-01-09 16:35:20 | dair-ai/ML-Papers-of-the-Week: 🔥Highlighting the top ML papers every week. | MSCE>ML

• 2023-01-07 07:24:38 | hpcaitech/ColossalAI: Colossal-AI: A Unified Deep Learning System for Big Model Era | MSCE>ML

• 2023-01-03 22:20:15 | How Shapley Values Work | MSCE>ML

• 2022-12-28 10:44:10 | nebuly-ai/nebullvm: Accelerate AI models leveraging best-of-breed optimization techniques 🚀 | MSCE>ML

• 2022-12-26 08:19:33 | High-Dimensional Probability: Course | MSCE>Stat

• 2022-12-26 07:47:28 | nebullvm/apps/accelerate/forward_forward at main · nebuly-ai/nebullvm · GitHub | MSCE>ML

• 2022-12-24 11:57:59 | Mathematics for Machine Learning | MSCE>ML

• 2022-12-22 06:43:51 | valeman/awesome-conformal-prediction: A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD and MSc theses, articles and open-source libraries. | MSCE>ML

• 2022-12-20 07:23:11 | Learn Prompting | Learn Prompting | MSCE>ML

• 2022-12-18 12:03:53 | CS 329S | Stanford | ML systems design | MSCE>ML

• 2022-11-10 03:56:36 | Forecasting: Principles and Practice (3rd ed) | MSCE>Stat

• 2022-10-26 10:49:21 | VoltaML/voltaML: ⚡VoltaML is a lightweight library to convert and run your ML/DL deep learning models in high performance inference runtimes like TensorRT, TorchScript, ONNX and TVM. | MSCE>ML

• 2022-10-17 11:08:30 | ML notes | MSCE>ML

• 2022-10-10 07:58:31 | Forecasting: theory and practice | MSCE>ML

• 2022-10-08 12:37:37 | How diffusion models work: the math from scratch | AI Summer | MSCE>ML

• 2022-10-08 12:17:34 | The Illustrated Stable Diffusion – Jay Alammar – Visualizing machine learning one concept at a time. | MSCE>ML

• 2022-10-02 12:30:25 | Course 2022: Full Stack Deep Learning | MSCE>ML

• 2022-10-02 12:01:53 | Interpretable Machine Learning | MSCE>ML

• 2022-09-23 06:27:16 | Introduction to Multilevel Modelling | MSCE>Stat

• 2022-09-21 09:59:58 | What are Diffusion Models? | Lil'Log | MSCE>ML

• 2022-09-13 11:46:21 | CS 11-711: Advanced NLP | MSCE>ML

• 2022-09-05 17:21:51 | tensorush/Awesome-Maths-Learning: Collection of the most awesome Maths learning resources in the form of notes, videos and cheatsheets. | MSCE>Math

• 2022-08-28 09:38:35 | What is the bias variance tradeoff? | R-bloggers | MSCE>ML

• 2022-08-28 09:38:16 | How to handle Imbalanced Data? | R-bloggers | MSCE>ML

• 2022-08-13 18:24:36 | Free Mathematics Books | MSCE>Math

• 2022-08-10 14:53:48 | Active Learning: Strategies, Tools, and Real-World Use Cases - neptune.ai | MSCE>ML

• 2022-08-09 21:09:30 | MLU-Explain | MSCE>ML

• 2022-08-06 07:46:45 | Transformers in computer vision: ViT architectures, tips, tricks and improvements | AI Summer | MSCE>ML

• 2022-08-01 03:04:33 | Lecture Notes on Neural Information Retrieval | MSCE>ML

• 2022-07-30 08:28:27 | https://probml.github.io/pml-book/book2.html | MSCE>ML

• 2022-07-27 02:17:31 | jonkrohn/ML-foundations: Machine Learning Foundations: Linear Algebra, Calculus, Statistics & Computer Science | MSCE>ML

• 2022-07-26 18:04:28 | GitHub - srush/Tensor-Puzzles: Solve puzzles. Improve your pytorch. | MSCE>ML

• 2022-07-26 18:03:21 | Practical Deep Learning for Coders 2022 · fast.ai | MSCE>ML

• 2022-07-24 11:35:08 | b7leung/MLE-Flashcards: 200+ detailed flashcards useful for reviewing topics in machine learning, computer vision, and computer science. | MSCE>ML

• 2022-07-23 14:51:22 | Introduction to Transformers | MSCE>ML

• 2022-07-21 08:23:15 | dformoso/machine-learning-mindmap: A mindmap summarising Machine Learning concepts, from Data Analysis to Deep Learning. | MSCE>ML

• 2022-07-18 10:01:02 | Measure theory in probability. Probability is not simple after all. | by Xichu Zhang | Towards Data Science | MSCE>Math

• 2022-07-18 10:00:38 | A guide to the Lebesgue measure and integration | by Xichu Zhang | Towards Data Science | MSCE>Math

• 2022-07-08 07:45:51 | An Introduction to Stochastic Processes | by Xichu Zhang | Jun, 2022 | Towards Data Science | MSCE>Math

• 2022-07-01 11:49:54 | Text Embeddings Visually Explained | MSCE>ML

• 2022-07-01 08:17:38 | michaelgutmann/ml-pen-and-paper-exercises: Pen and paper exercises in machine learning | MSCE>ML

• 2022-06-30 01:35:30 | ForeignGods/Sorting-Algorithms-Blender: Sorting algorithms visualized using the Blender Python API. | MSCE>CS

• 2022-06-22 08:49:03 | Algorithms & Data Structures | Super Study Guide | MSCE>CS

• 2022-06-17 08:47:14 | SIAM | Financial Mathematics and Engineering | MSCE>Math

• 2022-06-17 08:36:59 | SIAM | Home | MSCE>Math

• 2022-06-08 06:09:48 | A Beginner’s Guide to Q Learning - KDnuggets | MSCE>ML

• 2022-06-04 12:54:55 | dpressel/mint: MinT: Minimal Transformer Library and Tutorials | MSCE>ML

• 2022-05-26 20:04:38 | jbhuang0604/awesome-computer-vision: A curated list of awesome computer vision resources | MSCE>ML

• 2022-05-22 15:59:24 | ossu/math: 🧮 Path to a free self-taught education in Mathematics! | MSCE>Math

• 2022-05-21 13:09:44 | dair-ai/Mathematics-for-ML: 🧮 A collection of resources to learn mathematics for machine learning | MSCE>ML

• 2022-05-21 10:12:13 | Mathematical Logic through Python | MSCE>Math

• 2022-05-15 12:47:25 | josephmisiti/awesome-machine-learning: A curated list of awesome Machine Learning frameworks, libraries and software. | MSCE>ML

• 2022-05-14 07:22:42 | 5 Must-Do Error Analysis Before You Put Your Model in Production - neptune.ai | MSCE>ML

• 2022-05-07 11:49:54 | Nyandwi/machine_learning_complete: A comprehensive machine learning repository containing 30+ notebooks on different concepts, algorithms and techniques. | MSCE>ML

• 2022-05-06 07:16:10 | Walk with fastai | walkwithfastai | MSCE>ML

• 2022-05-03 07:44:53 | Bayes Rules! An Introduction to Applied Bayesian Modeling | MSCE>Stat

• 2022-04-28 20:23:13 | sebastianruder/NLP-progress: Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks. | MSCE>ML

• 2022-04-26 08:01:54 | rmcelreath/stat_rethinking_2022: Statistical Rethinking course winter 2022 | MSCE>Stat

• 2022-04-23 10:04:13 | Advances in Understanding, Improving, and Applying Contrastive Learning · Hazy Research | MSCE>ML

• 2022-04-23 09:17:19 | Efficient Deep Learning Book | Companion webpage to the book “Efficient Deep Learning”. | MSCE>ML

• 2022-04-23 00:38:37 | Awesome Bayes | MSCE>Stat

• 2022-04-22 23:10:10 | Learning Materials: computational social science | MSCE>ML

• 2022-04-20 14:36:49 | Time Series Projects: Tools, Packages, and Libraries That Can Help - neptune.ai | MSCE>ML

• 2022-04-19 19:28:57 | Improving Your Statistical Inferences | MSCE>Stat

• 2022-04-15 20:41:58 | Courses in reinforcement learning and sequential decision problems – Castle Labs | MSCE>ML

• 2022-04-07 21:53:50 | Exploring Neural Networks Visually in the Browser - Casey Primozic's Homepage | MSCE>ML

• 2022-04-03 15:57:12 | High-Dimensional Data Analysis by John Wright and Yi Ma | MSCE>ML

• 2022-04-02 11:30:45 | Mathematical Association of America | MSCE>Math

• 2022-04-02 11:30:32 | American Mathematical Society :: Homepage | MSCE>Math

• 2022-04-02 11:30:15 | American Statistical Association | MSCE>Math

• 2022-04-02 11:29:25 | Institute of Mathematical Statistics | Fostering the development and dissemination of the theory and applications of statistics and probability | MSCE>Math

• 2022-04-01 12:31:27 | One machine learning question every day - bnomial | MSCE>ML

• 2022-03-20 09:08:07 | [Book] Reinforcement Learning and Stochastic Optimization – Castle Labs | MSCE>ML

• 2022-03-20 09:00:55 | [Book] Sequential Decision Analytics and Modeling – Castle Labs | MSCE>ML

• 2022-03-19 09:39:53 | [2203.08890] The Mathematics of Artificial Intelligence | MSCE>ML

• 2022-03-19 09:31:57 | Keras Code examples | MSCE>ML

• 2022-03-19 09:31:12 | dair-ai/ML-Course-Notes: 🎓 Sharing course notes on all topics related to machine learning, NLP, and AI | MSCE>ML

• 2022-03-16 20:17:16 | Causality for Machine Learning | MSCE>ML

• 2022-02-23 09:55:57 | Monash Forecasting Repository | MSCE>ML

• 2022-02-22 10:09:04 | Deep Learning on Graphs | MSCE>ML

• 2022-02-11 10:48:01 | Think DSP | MSCE>Engg

• 2022-02-10 19:08:24 | Introduction to the A* Algorithm | MSCE>CS

• 2022-02-08 10:13:13 | Data Distribution Shifts and Monitoring | MSCE>ML

• 2022-02-04 19:37:56 | Natural Language Processing (NLP) for Semantic Search | Pinecone | MSCE>ML

• 2022-01-30 12:05:35 | ML and NLP Research Highlights of 2021 | MSCE>ML

• 2022-01-23 16:15:05 | Shapley Values - A Gentle Introduction | H2O.ai | MSCE>ML

• 2022-01-16 14:04:09 | Explainable ML — Alibi 0.6.2 documentation | MSCE>ML

• 2022-01-13 21:50:57 | Introduction to variational autoencoders | MSCE>ML

• 2022-01-13 20:14:43 | 70 Free Online Courses for Data Science to Advance Your Skills in 2022 | MSCE>ML

• 2022-01-13 09:17:43 | An Introduction to Autoencoders | MSCE>ML

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• 2021-09-10 09:48:52 | A friendly introduction to machine learning compilers and optimizers | MSCE>ML

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• 2021-08-31 07:38:09 | Complex Analysis | MSCE>Math

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• 2021-07-02 13:46:54 | https://www.google.com/url?q=https://u13167954.ct.sendgrid.net/ls/click?upn%3DI6s9-2BfQdKDIA9Bu-2B14T0ZDTwdbEDdv4EXCHWsboDl3szhsEnrkytjew-2B4Lh-2BR6Hrxvr5CbVemPXxoPSUZ2dFCLQzoq2fEgw1qYaizdDZWB9COYa9qBklrz-2FL4TXl-2Bt1-2FpiqqWIgPaoQlGDvWv6UmcJS6WUdo5tKbzkeMqGsbdak-3Dhwmr_-2BGZohrQsoJvSGAtbFvo2QqLI5GsEgLAgASfugK-2FRJf2Ws34iBT-2B-2B6aksWV4s5rwEFFM274CfWT4dxV2mVqjrJWvPljQ6V85UK0AvlKWt4q4JcxOnLX-2BFfC4cpUHryAW3gWcMWXfwKeOKMk4rYAt-2Fwpio8o6zDuDM-2B3uvWCW-2Fy6MGT11As2WRfgjFnkgNh-2B79Jx8zwKfee1stoO34Qytyboha3OybX9YVLR0L5Q0ZY4Xc7KAGmYbsOQ9NdKHCFIniEBY3lIBx-2BhWXOFxWAjUuuePQGAmWOb8nolGR732kS1hurDD2HMVwm-2FIxDzoAanzFvkO8jSto6T-2FUw508PQgiyBTSrImpuG2aNHQTd4BWjwRHFTjqZNZjQnIAJ0-2B2CdHgRIOIBfptZdulk1Qy1cNmZwP06g8Rc-2BFUt4WnYf1NWW5K9FHjdr94O5Kxy50DX53Q&source=gmail&ust=1625334403996000&usg=AFQjCNGHdnxUipriNLWiLnx52QhS8yWGRQ | MSCE>Stat

• 2021-06-29 07:50:14 | Introduction to Modern Statistics | MSCE>Stat

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• 2020-11-03 13:47:52 | How to make measure theory usable for your problem? | by Leonard Schuler | Oct, 2020 | Towards Data Science | MSCE>Stat

• 2020-10-29 08:36:11 | My Recommendations for Getting Started with NLP - Elvis's Blog | MSCE>ML

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• 2020-10-02 22:59:15 | [Book] Mathematics for Machine Learning | Companion webpage | MSCE>ML

• 2020-10-01 09:19:49 | The matrix calculus you need for deep learning | MSCE>ML

• 2020-09-28 08:41:19 | aiquizzes - learn ai | MSCE>ML

• 2020-09-17 19:52:11 | 24 Best (and Free) Books To Understand Machine Learning | MSCE>ML

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• 2020-01-23 11:14:48 | DDSP: Differentiable Digital Signal Processing | MSCE>Engg

• 2020-01-08 08:37:04 | Mathematical Analysis of Reinforcement Learning — Bellman Optimality Equation | MSCE>ML

• 2019-11-06 14:53:01 | Common Data Mistakes to Avoid | MSCE>ML

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• 2019-07-20 22:32:38 | Best Math Podcasts (2019) | MSCE>Math

• 2019-07-16 17:56:39 | benedekrozemberczki/awesome-gradient-boosting-papers: A curated list of gradient boosting research papers with implementations. | MSCE>ML

• 2019-07-16 17:56:30 | benedekrozemberczki/awesome-decision-tree-papers: A collection of research papers on decision, classification and regression trees with implementations. | MSCE>ML

• 2019-07-16 17:56:22 | benedekrozemberczki/awesome-graph-classification: A collection of important graph embedding, classification and representation learning papers with implementations. | MSCE>ML

• 2019-07-16 12:55:04 | Logistic Regression from Bayes' Theorem | MSCE>ML

• 2019-07-16 12:54:14 | mihail911/nlp-library | MSCE>ML

• 2019-07-09 13:34:11 | [Course]: A Code-First Introduction to Natural Language Processing · fast.ai | MSCE>ML

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• 2019-03-20 13:14:15 | Viewing Matrices & Probability as Graphs | MSCE>Stat

• 2019-03-16 22:48:10 | A gentle introduction to multithreading | MSCE>CS

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• 2015-08-21 14:00:42 | How a Kalman filter works, in pictures | MSCE>ML

• 2015-07-29 12:18:47 | Advanced R-Statistics | MSCE>Stat

• 2015-07-29 12:18:16 | Stats the Way I Like It | MSCE>Stat

• 2015-07-07 09:43:59 | Stress testing the EVPPI | MSCE>ML

• 2015-06-16 17:15:39 | An illustrated introduction to the t-SNE algorithm | MSCE>ML

• 2015-06-03 17:08:01 | Introduction to Support Vector Machines | MSCE>ML

• 2015-06-02 17:02:30 | Top 10 data mining algorithms in plain English | MSCE>ML

• 2015-05-04 10:06:36 | 40 Key Computer Science Concepts Explained In Layman’s Terms | MSCE>CS

• 2015-04-15 15:12:42 | Amazon Machine Learning – Make Data-Driven Decisions at Scale | MSCE>ML

• 2015-04-01 16:43:39 | Artificial Neurons | MSCE>ML

• 2015-03-06 11:22:47 | Circles Sines and Signals - Introduction to DSP | MSCE>Engg

• 2015-02-18 14:18:56 | Unsupervised Feature Learning and Deep Learning Tutorial | MSCE>ML

• 2015-02-06 16:56:45 | A Machine Learning Result | MSCE>ML

• 2014-12-26 17:45:25 | Principal Component Analysis on Imaging | MSCE>Stat

• 2014-12-26 17:36:18 | The Geometry of Classifiers | MSCE>ML

• 2014-12-07 15:02:49 | More on Prediction From Log-Linear Regressions | MSCE>Stat

• 2014-07-03 12:37:59 | A Tour of Machine Learning Algorithms | MSCE>ML

• 2014-06-06 17:12:21 | Tutorial: How to detect spurious correlations, and how to find the real ones | MSCE>Stat