Categories Choose a category: --Select a category-- statistics (28) traditional-machine-learning (57) applying-knowledge (196) deep-learning (38) reinforcement-learning (1) cnn (13) nlp (77) reading-research (31) mlops (63) theory (163) math (24) gnn (39) cloud (24) glaswegian (11) data-eng (55) mathematics (1) traditiona-machine-learning (4) mlop (1) statistics (28) (Day 314) Streamed + new video + more sklearn docs (Day 313) More of sklearn's 'user guide' (Day 312) Reading more of sklearn's Supervised learning doc (Day 296) Neo4j & LLM Fundamentals (Day 295) Starting a Credit Risk Modelling course on Udemy (Day 280) Reading scikit-learn docs on stream + reading Andryi Burkov's MLE book (Day 277) Neo4j Professional Certificate attempt 1 (Day 270) DE course by Joe Reis - completed (Day 268) Going back to some basics, math, and neo4j (Day 229) 'probabl' - a gem of a youtube channel (Day 228) Making a poster for the Not Google Devs Society (Day 227) Reading more about DL at scale (Day 166) Buying a new book + pseudocon in Seoul + using Gradio for a quick demo app (Day 131) Meeting for the Scottish dataset project + CS109 - Deep learning (Day 130) CS109 - MAP, Naive Bayes, Logistic Regression (Day 129) AI with a Scottish accent? + MLE lecture by Chris Piech (Stanford CS109) (Day 128) IBM Consulting Insights Virtual Careers event + More of CS109 (Day 127) Serving an API endpoint for news classification + Stanford's CS109 (Day 121) Uncovering the full reason behing multicollinearity + Frequent itemset mining lecture (Day 95) Designing a GNN layer + becoming a fellow of the Royal Statistical Society (Day 91) Probability - Multivariate models and Statistics chapters from Probabilistic Machine Learning (Day 90) Probability - Univariate Models and colab 0 from XCS224W - ML with Graphs (Day 89) More basics from ISLP (Day 88) Starting the book 'An Introduction to Statistical Learning' - Chapter 2 and 3 (Day 51) More of AI503 - High-dim space, random walks and markov chains, VC-dims (Day 50) My ML journey does not end today! - KAIST's AI503 Mathematics for AI (PCA, GMM, SVM) (Day 2) Finishing the ML course by Calctech (Pre-study) Some basics before starting my journey traditional-machine-learning (57) (Day 323) Predicting subway demand + Additive Dimensions (Day 319) Creating a practical ML notebook to evaluate a model based on some business statistic (Day 318) I'm a scikit-learn professional (Day 317) Streaming + more of Chip Huyen's book (Day 316) Reading more of the infamous Designing ML systems book (Day 315) Finally started reading Chip Huyen's Designing ML systems (Day 314) Streamed + new video + more sklearn docs (Day 313) More of sklearn's 'user guide' (Day 312) Reading more of sklearn's Supervised learning doc (Day 311) Checking out NODES '24, 10th chapter of LLM engineer's handbook, and linear models in sklearn (Day 310) I passed the Scikit-learn Associate Practitioner Certification (Day 309) Finishing scikit-learn's MOOC + AI fairness (Day 308) Streaming for 5 hours (overall) and reviewing sklearn's MOOC (Day 307) Covering sklearn's MOOC on stream (Day 306) Checking out scikit-learn's MOOC (Day 297) More about LLMs, and credit risk modelling (Day 296) Neo4j & LLM Fundamentals (Day 295) Starting a Credit Risk Modelling course on Udemy (Day 294) Reading more of sklearn's docs (Day 293) Finishing the book + reading sklearn docs for fun (Day 292) Continuing with the ML for financial risk management book (Day 291) Starting Machine Learning for Financial Risk Management with Python + some math for ML (Day 290) Finishing the book - Financial Data Engineering (Day 289) Reading more of the Fin. DE book + stream (Day 288) New book + scikit-learn's inspection module (Day 287) Calibrating classification models (Day 285) I was muted on stream :( + more LLM fine-tuning (Day 283) Reading more of the MLE book + fine-tuning llama models (Day 282) Chapter 5 from MLE by Andriy Burkov (Day 281) Read chapter 4 of Andriy Burkov's MLE book (Day 280) Reading scikit-learn docs on stream + reading Andryi Burkov's MLE book (Day 279) Neo4j Graph Data Science Certification - SUCCESS! (Day 278) Before Machine Learning Volume 2 - Calculus + Neo4j GDS (Day 277) Neo4j Professional Certificate attempt 1 (Day 243) Finishing a PySpark book (Day 237) More unsupervised learning algorithms + submitting the KB project ppt (Day 236) Reading about unsupervised learning algorithms + making the *final* version of our ppt videos (Day 232) Creating a script for the technical part of the KB project (Day 231) Advancing to the finals of the 6th Kukmin Bank Future Finance AI competition!!! (Day 230) Watching more educational videos from probabl (Day 229) 'probabl' - a gem of a youtube channel (Day 215) Trying out 'traditional' models on the KB project transaction fraud data (Day 182) Learning about feature selection in fraud detection and finding a classifier model with low recall (Day 124) MLx Fundamentals Day 1 - Intro to ML, Naive Bayes, Factorization methods (Day 18) Step 1 to TensorFlow Developer Certificate (Day 17) Microsoft's ML-For-Beginners - Classification and Clustering (Day 15) Quiz and practical lab from Andrew Ng's course (Day 14) Andrew Ng's Machine learning specialization (Day 13) I will be part of Korea Conference on Software Engineering 2024 (Day 8) Time series - finding trends (Day 7) Applying the time series knowledge to practice (Day 6) Time series course - Kaggle (Day 5) Kaggle's 'Learn' courses (Day 4) Looking for a ML book and Microsoft's ML-For-Beginners course (Day 3) Intro to time series (Day 2) Finishing the ML course by Calctech (Day 1) Machine learning course by Caltech applying-knowledge (196) (Day 325) Dimension modelling (Day 324) I secured a free spot at Zach Wilson's Jan 2025 data engineering bootcamp (Day 323) Predicting subway demand + Additive Dimensions (Day 322) Data Eng camp homework 1 completed (maybe) (Day 321) Slowly changing dimensions (Day 320) Day 1 of Zach Wilson's DE bootcamp - Data Modelling (Day 319) Creating a practical ML notebook to evaluate a model based on some business statistic (Day 318) I'm a scikit-learn professional (Day 317) Streaming + more of Chip Huyen's book (Day 316) Reading more of the infamous Designing ML systems book (Day 315) Finally started reading Chip Huyen's Designing ML systems (Day 314) Streamed + new video + more sklearn docs (Day 311) Checking out NODES '24, 10th chapter of LLM engineer's handbook, and linear models in sklearn (Day 310) I passed the Scikit-learn Associate Practitioner Certification (Day 309) Finishing scikit-learn's MOOC + AI fairness (Day 308) Streaming for 5 hours (overall) and reviewing sklearn's MOOC (Day 307) Covering sklearn's MOOC on stream (Day 306) Checking out scikit-learn's MOOC (Day 304) LLMs + some dbt (Day 303) Using my professor's A6000 GPU for LLM fine-tuning (Day 302) Data engineering pipeline from the LLM Engineer's handbook (Day 301) LLM Engineer's Handbook (Day 300) Trying out Neo4j's GraphRAG package (Day 299) ML in PySpark (Day 298) From Pandas to PySpark (Day 297) More about LLMs, and credit risk modelling (Day 296) Neo4j & LLM Fundamentals (Day 295) Starting a Credit Risk Modelling course on Udemy (Day 290) Finishing the book - Financial Data Engineering (Day 289) Reading more of the Fin. DE book + stream (Day 288) New book + scikit-learn's inspection module (Day 287) Calibrating classification models (Day 285) I was muted on stream :( + more LLM fine-tuning (Day 284) PEFT LLMs - Gemma, Mistral, Llama, Qwen (Day 283) Reading more of the MLE book + fine-tuning llama models (Day 276) 4.5hr stream learning about intermediate neo4j queries (Day 275) Finding new books to read + stream (Day 274) Finished the Graph Algorithms for Data Science book + stream (Day 273) First stream on youtube and finishing chapter 9 of the graph algs book (Day 270) DE course by Joe Reis - completed (Day 267) Continuing with the DE course by Joe Reis (Day 266) T5 model PEFT (Day 265) Day 6 of the DeepLearning.AI Data Engineering Professional Certificate course (Day 264) Day 5 of the DeepLearning.AI Data Engineering Professional Certificate course (Day 263) Day 4 of the DeepLearning.AI Data Engineering Professional Certificate course (Day 262) Day 3 of the DeepLearning.AI Data Engineering Professional Certificate course (Day 261) Day 2 of the DeepLearning.AI Data Engineering Professional Certificate course (Day 259) ML monitoring pipelines + Going deeper into neo4j (Day 256) Using, finetuning and explaining computer vision to my teammatest (Day 254) Started Intro to LangGraph by LangChain (Day 251) LingoMate and first hackathon completed (Day 250) Seoul Tech Impact Day 1 (Day 249) Seoul Tech Impact hackathon tomorrow (Day 248) New LLM project from my advisor (Day 246) First deployment on Kubernetes (Day 243) Finishing a PySpark book (Day 242) PySpark day (Day 241) Techniques for improving RAG pipes (Day 237) More unsupervised learning algorithms + submitting the KB project ppt (Day 236) Reading about unsupervised learning algorithms + making the *final* version of our ppt videos (Day 235) Re-recording the real-time pipeline video and getting final feedback on our ppt for the KB project (Day 234) Improving the Grafana dashboard and writing a final script for the KB project presentation (Day 233) Sending notifications for suspicious transactions to customers (Day 232) Creating a script for the technical part of the KB project (Day 231) Advancing to the finals of the 6th Kukmin Bank Future Finance AI competition!!! (Day 230) Watching more educational videos from probabl (Day 228) Making a poster for the Not Google Devs Society (Day 226) Your Personal Finance Assistant (Day 225) Starting the Finance Voice Assistant project (Day 217) KB project meeting and reading bank telemarketing papers (Day 216) Pipelines for XGBoost and CatBoost training, and using the models in the real-time inference pipeline (Day 214) The evaluation of my MLOps zoomcamp project arrived - max points (Day 213) Creating a grafana dashboard for the KB project (Day 212) Final Glaswegian TTS model (Day 211) 2 hour mark !!! Glaswegian dataset goal - accomplished! + whisper-small fine-tuned (Day 210) 118 minutes of Glaswegian accent audio clips (Day 209) Using Mage for pipeline orchestration in the KB project (Day 208) Setting up docker-services for the KB project, streaming transactions, and the Scottish dataset (Day 207) Finished with neo4j (for now) and thinking about fraud detection models (Day 206) Finishing the Stock Market Analysis zoomcamp (for now) (Day 205) Going back to a basic mlflow service and another meeting for the KB project (Day 204) Transaction data EDA + MLflow & minIO docker setup (Day 203) Starting LLM zoomcamp module 4 - Monitoring (Day 202) Setting up a Graph Convolution Network model to detect fraud credit card transactions (Day 201) Struggling with neo4j and a fraud GNN (Day 200) Kukmin Bank AI competition project idea (Day 198) Transactions Data Streaming Pipeline Porject (v1 completed) (Day 197) Learning about Kafka (Day 194) Using Video Generation Models for Taxi OD Demand Matrix Prediction (Day 192) Chapter 4 - Scaling with the compute layer (from the book - Effective Data Science Infrastructure) (Day 191) Starting the book - Effective Data Science Infrastructure (Day 190) Learning about evaluating vector search engines for RAG apps (Day 189) I finished the Car Insurance Fraud MLOps project. Thank you MLOps zoomcamp for teaching me so much! (Day 188) Setting up automatically updated monitoring UI using streamlit (Day 187) Setting up postgres, pgAdmin, Grafana and FastAPI to run in Docker (Day 186) Prefect cloud, model serving with FastAPI, and SHAP values (Day 185) Using prefect as my orchestrator for my MLOps project (Day 184) Mlflow experiment tracking and trying out metaflow (Day 182) Learning about feature selection in fraud detection and finding a classifier model with low recall (Day 181) Lending club data engineering project - Done (Day 180) From Kaggle to BigQuery dimension tables - an end2end pipeline (Day 179) Using Docker, Makefile, and starting Data modelling for my Lending club project (Day 178) Starting 'Lending club data engineering project' (Day 177) Spark for batch processing (Day 176) Testing, Documentation, Deployment with dbt and visualisations with Looker (Day 175) Learning about and using dbt cloud (Day 174) Starting LLM zoomcamp + Learning about Data Warehouses + BigQuery (Day 173) Terraform, GCP, virtual machines, data pipelines (Day 172) Learning about terraform + adding more data to the Glaswegian audio dataset (Day 171) Data engineering zoomcamp by DataTalksClub (Day 170) Uber data engineering project using GCP and Mage (Day 168) First day of Internship Experience UK - Technology by Bright network (Day 167) Learning about model monitoring (Day 166) Buying a new book + pseudocon in Seoul + using Gradio for a quick demo app (Day 165) Starting to use mlflow for my research's model tracking + homework 4 of the MLOps zoomcamp (Day 164) Learning about model deployment (and deleting AWS services) (Day 163) Reading about OD demand matrix prediction models (Day 162) Deploying a mage.ai instance to aws (Day 160) Simple data engineering pipeline with Prefect, and... MLOps with mage.ai (tons of problems) (Day 159) Learning and using prefect for MLOps orchestration (Day 156) Final XCS224W - ML with Graphs homework (Day 153) First steps into orchestration and ML pipelines (module 3 from MLOps zoomcamp) (Day 151) Reading more about taxi OD matrix prediction architectures + more Scottish dataset audio included (Day 150) Learning more about taxi OD matrix prediction + Scottish dataset update (Day 148) Microsoft Azure hackathon Day 2 (Day 147) Microsoft Azure hackathon Day 1 (Day 146)] MLOps zoomcamp module 2 homework + some more prep for Microsoft x NVIDIA's hackaton (Day 145) Build & Modernize AI Applications with Azure (prep for Microsoft Azure x NVIDIA hackaton in Seoul) (Day 143) Forward, backward prop and param update by hand (Day 142) Stanford's XCS224W - ML with Graphs - assignment 4 completed (Day 139) MLFlow (MLOps) on AWS (Day 138) Fine-tuning Speech T5 using a very small Glaswegian dataset (Day 135) Going deeper into MLOps (Day 134) Finished CS109 + Scottish dataset project + Started MLOps zoomcamp by DataTalks club (Day 133) Gathering data for the Scottish dataset project + Factor analysis + Grokking ML + MLxFundamentals Day 4 (2) (Day 131) Meeting for the Scottish dataset project + CS109 - Deep learning (Day 129) AI with a Scottish accent? + MLE lecture by Chris Piech (Stanford CS109) (Day 127) Serving an API endpoint for news classification + Stanford's CS109 (Day 126) Optimization lecture by Chi Jin from Princeton University + using Docker for the 1st time (Day 125) MLx Fundamentals Day 2 - Causal representation learning, optimization (Day 124) MLx Fundamentals Day 1 - Intro to ML, Naive Bayes, Factorization methods (Day 115) Exploring HuggingFace's capabilities and submitting 3rd homework from the ML with Graphs course (Day 112) db2chat - Talk with your (sqlite3) database (Day 108) Recommender systems + small adjustment to the text2chart webapp (Day 107) Transforming natural language to charts (Day 101) Doing the Google Cloud Digital Leader Learning Path (Day 100) Embeddings in practice + reading a couple of research papers + trying to deploy an LLM in production (Day 98) Finishing XCS224W - ML with Graphs' 2nd homework on GNNs Using PyTorch Geometric (Day 97) Review of the GNN structure and training (last 2 days) + starting Colab 2 of XCS224W - ML with Graphs (Day 94) Link analysis page rank random walks + First assignment + Short intro to GNNs (Day 90) Probability - Univariate Models and colab 0 from XCS224W - ML with Graphs (Day 87) Registered for Stanford's XCS224W - Machine Learning with Graphs + RAG webapp with llama-index tutorial (Day 86) Made a youtube video - Chat with your PDF for free in colab using huggingface, mongodb, llama_index, langchain (Day 83) Summary of my PDF RAG from scratch (Day 82) Looking for better parsing methods and prompting techniques for my PDF RAG (Day 81) RAG from scratch - chunking is very important! (Day 80) Starting to write my own RAG from scratch on a bank's T&C pdf (Day 79) Attempting to make a Local Retrieval Augmented Generation (RAG) from Scratch (Day 72) Carnegie Mellon University - Advanced NLP Spring 2024 - assignment 1 (Day 71) Backprop, GELU, Tricking ChatGPT, and Stealing part of an LLM (Day 70) Testing my backprop knowledge (Day 69) Training an LLM to generate Harry Potter text (Day 68) Build a LLM from scratch chapter 4 - making the GPT-2 architecture (Day 67) Build a LLM from scratch chapter 3 - self-attention from scratch (Day 66) Starting Build a LLM from scratch by Sebastian Raschka (Day 61) Stanford CS224N (NLP with DL) - Machine translation, seq2seq + a side CDCGAN mini project (Day 58) Stanford CS224N (NLP with DL) Lecture 2 - Neural classifiers (diving deeper into word embeddings) (Day 57) Stanford CS224N - Lecture 1. Word vectors (Day 56) I found my next step in the ladder - cs224n NLP with DL by Stanford (Day 55) Learning about tokenization in LLMs (Day 54) I became a backprop ninja! (woohoo) (Day 53) Getting closer to becoming a 'backprop ninja' (thanks to Stanford Uni's cs231n assignments) (Day 52) Learning more about transformers with Andrej Karpathy (Day 47) Learning a bit more about GANs and finding more KAIST courses (Day 46) Meeting Transformers again and their implementation (Day 45) Trying to understand VAEs with Professor Choi from KAIST (Day 44) Batch vs Layer vs Group Normalization and GANs (+ found a free KAIST AI course) (Day 43) Coding up LeNet, VGG, InceptionNet, UNet from scratch (Day 42) Creating a UNet with PyTorch (Day 41) A bit advanced computer vision concept review (Day 39) Reading papers of powerful CNN models + going back to basics (+ some more Andrej Karpathy lectures) (Day 38) Traffic sign classification and bbox model-PyTorch and more of Andrej Karpathy's talks (Day 25) Using neural nets for time series predictions (Day 24) Using neural nets for time series predictions (Day 23) Tried making an Natural Language Processing model (Day 22) Created a Card image classifier model using Neural networks (Day 21) (Almost) finished prep for TensorFlow developer certificate (Day 17) Microsoft's ML-For-Beginners - Classification and Clustering (Day 15) Quiz and practical lab from Andrew Ng's course (Day 12) Neural network model for bank churn prediction and making a webapp for exchange rate pred (Day 11) Joined a 'bank churn prediction' competition on Kaggle (Day 10) Simple linear regression to predict GCPA (Day 9) Simple linear regression to predict GCPA (Day 8) Time series - finding trends (Day 7) Applying the time series knowledge to practice (Day 6) Time series course - Kaggle deep-learning (38) (Day 256) Using, finetuning and explaining computer vision to my teammatest (Day 255) The Little Book of Deep Learning (Day 239) 7th place at the KB future finance competition 🥳 (Day 238) Rehearsal for the KB AI competition (Day 227) Reading more about DL at scale (Day 224) Learning about Snowflake and starting the book - Deep Learning at Scale (Day 194) Using Video Generation Models for Taxi OD Demand Matrix Prediction (Day 143) Forward, backward prop and param update by hand (Day 133) Gathering data for the Scottish dataset project + Factor analysis + Grokking ML + MLxFundamentals Day 4 (2) (Day 132) MLx Fundamentals Day 4(1) + CS109 - Fairness in AI (Day 125) MLx Fundamentals Day 2 - Causal representation learning, optimization (Day 124) MLx Fundamentals Day 1 - Intro to ML, Naive Bayes, Factorization methods (Day 123] Optimization algorithms chapter from Dive into DL (Day 122) Dive into Deep Learning - Interactive deep learning book with code, math, and discussions (Day 120) Starting Stanford's CS246 - Mining Massive Datasets + MIT's Intro DL (Day 37) PyTorch traffic sign classification and detection model (Day 36) Intro to PyTorch (Day 35) TensorFlow Advanced techniques (Day 33) Tensorflow deployment specialization and a webapp for recognizing the Korean alphabet (Day 32) Language Transformers and KCSE day 3 (Day 31) Natural Language Processing and KCSE day 2 (Day 30) KCSE 2024 day 1 and Face recognition & Neural Style transfer (Day 29) How to do object detection? (Day 28) Diving deeper into Convolutional Neural Networks (Day 27) Improving deep learning models (Day 26) Deep Learning Specialization course by Andrew Ng (Day 25) Using neural nets for time series predictions (Day 24) Using neural nets for time series predictions (Day 23) Tried making an Natural Language Processing model (Day 22) Created a Card image classifier model using Neural networks (Day 21) (Almost) finished prep for TensorFlow developer certificate (Day 20) Natural Language Processing - TensorFlow Developer Certificate Part 3 (Day 19) TensorFlow Developer Certificate Part 2 - Convolutional Neural Networks in TensorFlow (Day 18) Step 1 to TensorFlow Developer Certificate (Day 16) Quiz and practical lab from Andrew Ng's course (Day 15) Quiz and practical lab from Andrew Ng's course (Day 14) Andrew Ng's Machine learning specialization (Day 12) Neural network model for bank churn prediction and making a webapp for exchange rate pred reinforcement-learning (1) (Day 16) Quiz and practical lab from Andrew Ng's course cnn (13) (Day 61) Stanford CS224N (NLP with DL) - Machine translation, seq2seq + a side CDCGAN mini project (Day 43) Coding up LeNet, VGG, InceptionNet, UNet from scratch (Day 42) Creating a UNet with PyTorch (Day 41) A bit advanced computer vision concept review (Day 38) Traffic sign classification and bbox model-PyTorch and more of Andrej Karpathy's talks (Day 37) PyTorch traffic sign classification and detection model (Day 36) Intro to PyTorch (Day 33) Tensorflow deployment specialization and a webapp for recognizing the Korean alphabet (Day 30) KCSE 2024 day 1 and Face recognition & Neural Style transfer (Day 29) How to do object detection? (Day 28) Diving deeper into Convolutional Neural Networks (Day 22) Created a Card image classifier model using Neural networks (Day 19) TensorFlow Developer Certificate Part 2 - Convolutional Neural Networks in TensorFlow nlp (77) (Day 325) Dimension modelling (Day 319) Creating a practical ML notebook to evaluate a model based on some business statistic (Day 318) I'm a scikit-learn professional (Day 315) Finally started reading Chip Huyen's Designing ML systems (Day 306) Checking out scikit-learn's MOOC (Day 305) More of the LLM Engineer's handbook (Day 304) LLMs + some dbt (Day 303) Using my professor's A6000 GPU for LLM fine-tuning (Day 302) Data engineering pipeline from the LLM Engineer's handbook (Day 301) LLM Engineer's Handbook (Day 300) Trying out Neo4j's GraphRAG package (Day 299) ML in PySpark (Day 297) More about LLMs, and credit risk modelling (Day 287) Calibrating classification models (Day 285) I was muted on stream :( + more LLM fine-tuning (Day 284) PEFT LLMs - Gemma, Mistral, Llama, Qwen (Day 283) Reading more of the MLE book + fine-tuning llama models (Day 275) Finding new books to read + stream (Day 266) T5 model PEFT (Day 257) LangChain's intro to LangGraph part 2 (Day 254) Started Intro to LangGraph by LangChain (Day 253) Designing effective ML monitoring with EvidentlyAI Part 2 (Day 252) Designing effective ML monitoring with EvidentlyAI (Day 251) LingoMate and first hackathon completed (Day 250) Seoul Tech Impact Day 1 (Day 249) Seoul Tech Impact hackathon tomorrow (Day 248) New LLM project from my advisor (Day 244) Streaming databases book + advanced RAG techniques (Day 241) Techniques for improving RAG pipes (Day 226) Your Personal Finance Assistant (Day 225) Starting the Finance Voice Assistant project (Day 219) Fundamentals of Data Eng and LLM data preprocessing pipelines in Mage (Day 218) ML canvas for the KB fraud transaction detection project (Day 203) Starting LLM zoomcamp module 4 - Monitoring (Day 199) Continuing with Build an LLM from scratch (Day 190) Learning about evaluating vector search engines for RAG apps (Day 174) Starting LLM zoomcamp + Learning about Data Warehouses + BigQuery (Day 134) Finished CS109 + Scottish dataset project + Started MLOps zoomcamp by DataTalks club (Day 115) Exploring HuggingFace's capabilities and submitting 3rd homework from the ML with Graphs course (Day 112) db2chat - Talk with your (sqlite3) database (Day 107) Transforming natural language to charts (Day 100) Embeddings in practice + reading a couple of research papers + trying to deploy an LLM in production (Day 98) Finishing XCS224W - ML with Graphs' 2nd homework on GNNs Using PyTorch Geometric (Day 87) Registered for Stanford's XCS224W - Machine Learning with Graphs + RAG webapp with llama-index tutorial (Day 86) Made a youtube video - Chat with your PDF for free in colab using huggingface, mongodb, llama_index, langchain (Day 84) Lecture 13 and 14 of CMU 11-711's Advanced NLP - Debugging and model interpretation; Ensembling methods (Day 83) Summary of my PDF RAG from scratch (Day 82) Looking for better parsing methods and prompting techniques for my PDF RAG (Day 81) RAG from scratch - chunking is very important! (Day 80) Starting to write my own RAG from scratch on a bank's T&C pdf (Day 79) Attempting to make a Local Retrieval Augmented Generation (RAG) from Scratch (Day 78) NVIDIA GTC talks + accepted to Stanford AI professional certificate + PERL (Day 77) Review of the ACL 2023 talk, and lecture 10 from CMU's advanced NLP course about retrieval models (Day 76) Finishing the Retrieval-based LM talk, and learning about distillation, quantization and pruning (Day 75) Retrieval-based LMs training and applications (Day 74) Retrieval-based LMs (Day 73) MBR and FUDGE - decoding mechanisms; pre vs post layer normalization (Day 72) Carnegie Mellon University - Advanced NLP Spring 2024 - assignment 1 (Day 71) Backprop, GELU, Tricking ChatGPT, and Stealing part of an LLM (Day 70) Testing my backprop knowledge (Day 69) Training an LLM to generate Harry Potter text (Day 68) Build a LLM from scratch chapter 4 - making the GPT-2 architecture (Day 67) Build a LLM from scratch chapter 3 - self-attention from scratch (Day 66) Starting Build a LLM from scratch by Sebastian Raschka (Day 65) Stanford CS224N (NLP with DL) - Multimodal DL and Model analysis and explanation (Day 64) Stanford CS224N (NLP with DL) - Coreference resolution, Adding knowledge to LMs, Code generation (Day 63) Stanford CS224N (NLP with DL) - Natural Language Generation, Question Answering (Day 62) Stanford CS224N (NLP with DL) - Transformers, Pretraining, RLHF (Day 60) Stanford CS224N (NLP with DL) - Language modelling, RNNs and LSTMs (Day 59) Stanford CS224N (NLP with DL) - Backprop and Dependency Parsing (Day 57) Stanford CS224N - Lecture 1. Word vectors (Day 52) Learning more about transformers with Andrej Karpathy (Day 46) Meeting Transformers again and their implementation (Day 32) Language Transformers and KCSE day 3 (Day 31) Natural Language Processing and KCSE day 2 (Day 23) Tried making an Natural Language Processing model (Day 20) Natural Language Processing - TensorFlow Developer Certificate Part 3 reading-research (31) (Day 217) KB project meeting and reading bank telemarketing papers (Day 207) Finished with neo4j (for now) and thinking about fraud detection models (Day 195) Reading about bank term deposit subscription prediction models (Day 194) Using Video Generation Models for Taxi OD Demand Matrix Prediction (Day 170) Uber data engineering project using GCP and Mage (Day 169) Writing first version of introduction for my paper + day 2 of IEUK (Day 165) Starting to use mlflow for my research's model tracking + homework 4 of the MLOps zoomcamp (Day 163) Reading about OD demand matrix prediction models (Day 162) Deploying a mage.ai instance to aws (Day 161) Learning about GANs' use in generating OD demand matrix (Day 158) 50 minutes of audio in the Scottish dataset + exploring Mixture Density Networks in GNNs (Day 157) GNN design choices and starting an MLOps book on manning.com (Day 156) Final XCS224W - ML with Graphs homework (Day 155) Reading more about 'historic' (used as baseline) models for spatio-temporal predictions using graphs (Day 154) Diving deeper into Graph Neural Networks used in taxi demand prediction (Day 152) SVR & STTCM - Two architectures for taxi demand prediction (Day 151) Reading more about taxi OD matrix prediction architectures + more Scottish dataset audio included (Day 150) Learning more about taxi OD matrix prediction + Scottish dataset update (Day 149) Learning about the Origin-Destination Matrix Prediction problem in passenger prediction tasks (Day 144) Using Graph Neural Networks to predict taxi passenger demand and origin/destination (Day 119) Graph Convolutional Transformer application on electronic health records (Day 116) Reading some research + transferring posts to the new blog (Day 100) Embeddings in practice + reading a couple of research papers + trying to deploy an LLM in production (Day 85) How to write a great research paper (Day 78) NVIDIA GTC talks + accepted to Stanford AI professional certificate + PERL (Day 73) MBR and FUDGE - decoding mechanisms; pre vs post layer normalization (Day 71) Backprop, GELU, Tricking ChatGPT, and Stealing part of an LLM (Day 58) Stanford CS224N (NLP with DL) Lecture 2 - Neural classifiers (diving deeper into word embeddings) (Day 39) Reading papers of powerful CNN models + going back to basics (+ some more Andrej Karpathy lectures) (Day 31) Natural Language Processing and KCSE day 2 (Day 30) KCSE 2024 day 1 and Face recognition & Neural Style transfer mlops (63) (Day 319) Creating a practical ML notebook to evaluate a model based on some business statistic (Day 318) I'm a scikit-learn professional (Day 317) Streaming + more of Chip Huyen's book (Day 316) Reading more of the infamous Designing ML systems book (Day 315) Finally started reading Chip Huyen's Designing ML systems (Day 287) Calibrating classification models (Day 286) MLE - Model deployment from Andriy Burkov (Day 283) Reading more of the MLE book + fine-tuning llama models (Day 282) Chapter 5 from MLE by Andriy Burkov (Day 281) Read chapter 4 of Andriy Burkov's MLE book (Day 280) Reading scikit-learn docs on stream + reading Andryi Burkov's MLE book (Day 259) ML monitoring pipelines + Going deeper into neo4j (Day 253) Designing effective ML monitoring with EvidentlyAI Part 2 (Day 252) Designing effective ML monitoring with EvidentlyAI (Day 247) Continuing with AI monitoring using EvidentlyAI (Day 246) First deployment on Kubernetes (Day 245) Streaming dbs + EvidentlyAI course (Day 241) Techniques for improving RAG pipes (Day 240) Example for transitioning from Docker to K8s (Day 235) Re-recording the real-time pipeline video and getting final feedback on our ppt for the KB project (Day 234) Improving the Grafana dashboard and writing a final script for the KB project presentation (Day 233) Sending notifications for suspicious transactions to customers (Day 232) Creating a script for the technical part of the KB project (Day 224) Learning about Snowflake and starting the book - Deep Learning at Scale (Day 223) Finishing up Introducing MLOps (Day 222) Fundamentals of Data Engineering and Introducing MLOps in O'Reilly (Day 219) Fundamentals of Data Eng and LLM data preprocessing pipelines in Mage (Day 218) ML canvas for the KB fraud transaction detection project (Day 217) KB project meeting and reading bank telemarketing papers (Day 216) Pipelines for XGBoost and CatBoost training, and using the models in the real-time inference pipeline (Day 214) The evaluation of my MLOps zoomcamp project arrived - max points (Day 213) Creating a grafana dashboard for the KB project (Day 209) Using Mage for pipeline orchestration in the KB project (Day 208) Setting up docker-services for the KB project, streaming transactions, and the Scottish dataset (Day 205) Going back to a basic mlflow service and another meeting for the KB project (Day 204) Transaction data EDA + MLflow & minIO docker setup (Day 202) Setting up a Graph Convolution Network model to detect fraud credit card transactions (Day 201) Struggling with neo4j and a fraud GNN (Day 196) Learned about 'ML canvas' and more about MLOps (Day 193) Chapter 5, 6, and 7 from Effective Data Science Infrastructure (Day 192) Chapter 4 - Scaling with the compute layer (from the book - Effective Data Science Infrastructure) (Day 191) Starting the book - Effective Data Science Infrastructure (Day 189) I finished the Car Insurance Fraud MLOps project. Thank you MLOps zoomcamp for teaching me so much! (Day 188) Setting up automatically updated monitoring UI using streamlit (Day 187) Setting up postgres, pgAdmin, Grafana and FastAPI to run in Docker (Day 186) Prefect cloud, model serving with FastAPI, and SHAP values (Day 185) Using prefect as my orchestrator for my MLOps project (Day 184) Mlflow experiment tracking and trying out metaflow (Day 183) Failing to install Kubeflow, and setting up mlflow on GCP (Day 167) Learning about model monitoring (Day 165) Starting to use mlflow for my research's model tracking + homework 4 of the MLOps zoomcamp (Day 164) Learning about model deployment (and deleting AWS services) (Day 162) Deploying a mage.ai instance to aws (Day 160) Simple data engineering pipeline with Prefect, and... MLOps with mage.ai (tons of problems) (Day 159) Learning and using prefect for MLOps orchestration (Day 157) GNN design choices and starting an MLOps book on manning.com (Day 153) First steps into orchestration and ML pipelines (module 3 from MLOps zoomcamp) (Day 139) MLFlow (MLOps) on AWS (Day 135) Going deeper into MLOps (Day 134) Finished CS109 + Scottish dataset project + Started MLOps zoomcamp by DataTalks club (Day 101) Doing the Google Cloud Digital Leader Learning Path (Day 34) Deploying ML models and TensorBoard (Day 33) Tensorflow deployment specialization and a webapp for recognizing the Korean alphabet theory (163) (Day 325) Dimension modelling (Day 323) Predicting subway demand + Additive Dimensions (Day 321) Slowly changing dimensions (Day 320) Day 1 of Zach Wilson's DE bootcamp - Data Modelling (Day 318) I'm a scikit-learn professional (Day 317) Streaming + more of Chip Huyen's book (Day 316) Reading more of the infamous Designing ML systems book (Day 315) Finally started reading Chip Huyen's Designing ML systems (Day 314) Streamed + new video + more sklearn docs (Day 313) More of sklearn's 'user guide' (Day 312) Reading more of sklearn's Supervised learning doc (Day 311) Checking out NODES '24, 10th chapter of LLM engineer's handbook, and linear models in sklearn (Day 310) I passed the Scikit-learn Associate Practitioner Certification (Day 309) Finishing scikit-learn's MOOC + AI fairness (Day 308) Streaming for 5 hours (overall) and reviewing sklearn's MOOC (Day 307) Covering sklearn's MOOC on stream (Day 306) Checking out scikit-learn's MOOC (Day 305) More of the LLM Engineer's handbook (Day 297) More about LLMs, and credit risk modelling (Day 296) Neo4j & LLM Fundamentals (Day 295) Starting a Credit Risk Modelling course on Udemy (Day 294) Reading more of sklearn's docs (Day 293) Finishing the book + reading sklearn docs for fun (Day 292) Continuing with the ML for financial risk management book (Day 291) Starting Machine Learning for Financial Risk Management with Python + some math for ML (Day 290) Finishing the book - Financial Data Engineering (Day 289) Reading more of the Fin. DE book + stream (Day 288) New book + scikit-learn's inspection module (Day 287) Calibrating classification models (Day 286) MLE - Model deployment from Andriy Burkov (Day 285) I was muted on stream :( + more LLM fine-tuning (Day 283) Reading more of the MLE book + fine-tuning llama models (Day 282) Chapter 5 from MLE by Andriy Burkov (Day 281) Read chapter 4 of Andriy Burkov's MLE book (Day 280) Reading scikit-learn docs on stream + reading Andryi Burkov's MLE book (Day 275) Finding new books to read + stream (Day 274) Finished the Graph Algorithms for Data Science book + stream (Day 273) First stream on youtube and finishing chapter 9 of the graph algs book (Day 272) A bit more of Graph Algorithms for Data Science (Day 271) DE - insight and advice from industry experts (Day 270) DE course by Joe Reis - completed (Day 269) The math behind neural nets + trying the capstone project from DL.AI's DE specialisation (Day 268) Going back to some basics, math, and neo4j (Day 267) Continuing with the DE course by Joe Reis (Day 265) Day 6 of the DeepLearning.AI Data Engineering Professional Certificate course (Day 264) Day 5 of the DeepLearning.AI Data Engineering Professional Certificate course (Day 263) Day 4 of the DeepLearning.AI Data Engineering Professional Certificate course (Day 262) Day 3 of the DeepLearning.AI Data Engineering Professional Certificate course (Day 261) Day 2 of the DeepLearning.AI Data Engineering Professional Certificate course (Day 259) ML monitoring pipelines + Going deeper into neo4j (Day 258) Math exercises in ML (Day 257) LangChain's intro to LangGraph part 2 (Day 255) The Little Book of Deep Learning (Day 254) Started Intro to LangGraph by LangChain (Day 247) Continuing with AI monitoring using EvidentlyAI (Day 246) First deployment on Kubernetes (Day 245) Streaming dbs + EvidentlyAI course (Day 244) Streaming databases book + advanced RAG techniques (Day 243) Finishing a PySpark book (Day 242) PySpark day (Day 240) Example for transitioning from Docker to K8s (Day 239) 7th place at the KB future finance competition 🥳 (Day 238) Rehearsal for the KB AI competition (Day 237) More unsupervised learning algorithms + submitting the KB project ppt (Day 236) Reading about unsupervised learning algorithms + making the *final* version of our ppt videos (Day 231) Advancing to the finals of the 6th Kukmin Bank Future Finance AI competition!!! (Day 230) Watching more educational videos from probabl (Day 229) 'probabl' - a gem of a youtube channel (Day 228) Making a poster for the Not Google Devs Society (Day 227) Reading more about DL at scale (Day 224) Learning about Snowflake and starting the book - Deep Learning at Scale (Day 223) Finishing up Introducing MLOps (Day 222) Fundamentals of Data Engineering and Introducing MLOps in O'Reilly (Day 220) Chapter 2 The Data Engineering Lifecycle (Day 219) Fundamentals of Data Eng and LLM data preprocessing pipelines in Mage (Day 199) Continuing with Build an LLM from scratch (Day 196) Learned about 'ML canvas' and more about MLOps (Day 193) Chapter 5, 6, and 7 from Effective Data Science Infrastructure (Day 192) Chapter 4 - Scaling with the compute layer (from the book - Effective Data Science Infrastructure) (Day 191) Starting the book - Effective Data Science Infrastructure (Day 156) Final XCS224W - ML with Graphs homework (Day 146)] MLOps zoomcamp module 2 homework + some more prep for Microsoft x NVIDIA's hackaton (Day 145) Build & Modernize AI Applications with Azure (prep for Microsoft Azure x NVIDIA hackaton in Seoul) (Day 141) Lognormal random variables and looking for a TA position (Day 140) First 3 chapters of A Primer For The Mathematics Of Financial Engineering by Dan Stefanica (Day 135) Going deeper into MLOps (Day 134) Finished CS109 + Scottish dataset project + Started MLOps zoomcamp by DataTalks club (Day 133) Gathering data for the Scottish dataset project + Factor analysis + Grokking ML + MLxFundamentals Day 4 (2) (Day 132) MLx Fundamentals Day 4(1) + CS109 - Fairness in AI (Day 131) Meeting for the Scottish dataset project + CS109 - Deep learning (Day 130) CS109 - MAP, Naive Bayes, Logistic Regression (Day 129) AI with a Scottish accent? + MLE lecture by Chris Piech (Stanford CS109) (Day 128) IBM Consulting Insights Virtual Careers event + More of CS109 (Day 127) Serving an API endpoint for news classification + Stanford's CS109 (Day 126) Optimization lecture by Chi Jin from Princeton University + using Docker for the 1st time (Day 125) MLx Fundamentals Day 2 - Causal representation learning, optimization (Day 124) MLx Fundamentals Day 1 - Intro to ML, Naive Bayes, Factorization methods (Day 123] Optimization algorithms chapter from Dive into DL (Day 122) Dive into Deep Learning - Interactive deep learning book with code, math, and discussions (Day 121) Uncovering the full reason behing multicollinearity + Frequent itemset mining lecture (Day 120) Starting Stanford's CS246 - Mining Massive Datasets + MIT's Intro DL (Day 117) Some linear algebra + eigenvector/values and transferring more posts to the new blog (Day 114) Trustworthy Graph AI (Day 113) Making a better blog + Geometric Graph Learning for Biology (Day 111) Advanced Topics in GNNs (Day 110) Learning about Graph Transformers (Day 109) Graph Generative Models (Day 108) Recommender systems + small adjustment to the text2chart webapp (Day 106) Community structure in networks (Day 105) Network subgraph counting and matching (Day 104) Reasoning over Knowledge Graphs (Day 103) Knowledge Graphs (Day 102) Label propagation in ML with Graphs (Day 99) XCS224W - ML with Graphs - Theory of GNNs (Day 98) Finishing XCS224W - ML with Graphs' 2nd homework on GNNs Using PyTorch Geometric (Day 97) Review of the GNN structure and training (last 2 days) + starting Colab 2 of XCS224W - ML with Graphs (Day 96) GNN Training Pipeline + looking for opportunities this summer (Day 95) Designing a GNN layer + becoming a fellow of the Royal Statistical Society (Day 94) Link analysis page rank random walks + First assignment + Short intro to GNNs (Day 93) Node embeddings in graphs + some foundational statistics/math (Day 92) Starting the official Stanford XCS224W - ML with Graphs (Day 91) Probability - Multivariate models and Statistics chapters from Probabilistic Machine Learning (Day 90) Probability - Univariate Models and colab 0 from XCS224W - ML with Graphs (Day 89) More basics from ISLP (Day 88) Starting the book 'An Introduction to Statistical Learning' - Chapter 2 and 3 (Day 87) Registered for Stanford's XCS224W - Machine Learning with Graphs + RAG webapp with llama-index tutorial (Day 85) How to write a great research paper (Day 84) Lecture 13 and 14 of CMU 11-711's Advanced NLP - Debugging and model interpretation; Ensembling methods (Day 78) NVIDIA GTC talks + accepted to Stanford AI professional certificate + PERL (Day 77) Review of the ACL 2023 talk, and lecture 10 from CMU's advanced NLP course about retrieval models (Day 76) Finishing the Retrieval-based LM talk, and learning about distillation, quantization and pruning (Day 75) Retrieval-based LMs training and applications (Day 74) Retrieval-based LMs (Day 73) MBR and FUDGE - decoding mechanisms; pre vs post layer normalization (Day 71) Backprop, GELU, Tricking ChatGPT, and Stealing part of an LLM (Day 69) Training an LLM to generate Harry Potter text (Day 68) Build a LLM from scratch chapter 4 - making the GPT-2 architecture (Day 67) Build a LLM from scratch chapter 3 - self-attention from scratch (Day 66) Starting Build a LLM from scratch by Sebastian Raschka (Day 65) Stanford CS224N (NLP with DL) - Multimodal DL and Model analysis and explanation (Day 64) Stanford CS224N (NLP with DL) - Coreference resolution, Adding knowledge to LMs, Code generation (Day 63) Stanford CS224N (NLP with DL) - Natural Language Generation, Question Answering (Day 62) Stanford CS224N (NLP with DL) - Transformers, Pretraining, RLHF (Day 61) Stanford CS224N (NLP with DL) - Machine translation, seq2seq + a side CDCGAN mini project (Day 60) Stanford CS224N (NLP with DL) - Language modelling, RNNs and LSTMs (Day 59) Stanford CS224N (NLP with DL) - Backprop and Dependency Parsing (Day 58) Stanford CS224N (NLP with DL) Lecture 2 - Neural classifiers (diving deeper into word embeddings) (Day 57) Stanford CS224N - Lecture 1. Word vectors (Day 56) I found my next step in the ladder - cs224n NLP with DL by Stanford (Day 55) Learning about tokenization in LLMs (Day 54) I became a backprop ninja! (woohoo) (Day 53) Getting closer to becoming a 'backprop ninja' (thanks to Stanford Uni's cs231n assignments) (Day 52) Learning more about transformers with Andrej Karpathy (Day 51) More of AI503 - High-dim space, random walks and markov chains, VC-dims (Day 50) My ML journey does not end today! - KAIST's AI503 Mathematics for AI (PCA, GMM, SVM) (Day 49) KAIST's AI503 Mathematics for AI (Continuous optimization, When models meet data, Linear regression) (Day 48) KAIST's AI503 Mathematics for AI (Matrix Decompositions) (Day 47) Learning a bit more about GANs and finding more KAIST courses (Day 46) Meeting Transformers again and their implementation (Day 45) Trying to understand VAEs with Professor Choi from KAIST (Day 44) Batch vs Layer vs Group Normalization and GANs (+ found a free KAIST AI course) (Day 41) A bit advanced computer vision concept review (Day 40) Starting a Self-driving cars course by the University of Toronto math (24) (Day 295) Starting a Credit Risk Modelling course on Udemy (Day 279) Neo4j Graph Data Science Certification - SUCCESS! (Day 278) Before Machine Learning Volume 2 - Calculus + Neo4j GDS (Day 275) Finding new books to read + stream (Day 270) DE course by Joe Reis - completed (Day 269) The math behind neural nets + trying the capstone project from DL.AI's DE specialisation (Day 268) Going back to some basics, math, and neo4j (Day 258) Math exercises in ML (Day 143) Forward, backward prop and param update by hand (Day 141) Lognormal random variables and looking for a TA position (Day 140) First 3 chapters of A Primer For The Mathematics Of Financial Engineering by Dan Stefanica (Day 126) Optimization lecture by Chi Jin from Princeton University + using Docker for the 1st time (Day 125) MLx Fundamentals Day 2 - Causal representation learning, optimization (Day 121) Uncovering the full reason behing multicollinearity + Frequent itemset mining lecture (Day 117) Some linear algebra + eigenvector/values and transferring more posts to the new blog (Day 93) Node embeddings in graphs + some foundational statistics/math (Day 56) I found my next step in the ladder - cs224n NLP with DL by Stanford (Day 55) Learning about tokenization in LLMs (Day 54) I became a backprop ninja! (woohoo) (Day 53) Getting closer to becoming a 'backprop ninja' (thanks to Stanford Uni's cs231n assignments) (Day 51) More of AI503 - High-dim space, random walks and markov chains, VC-dims (Day 50) My ML journey does not end today! - KAIST's AI503 Mathematics for AI (PCA, GMM, SVM) (Day 49) KAIST's AI503 Mathematics for AI (Continuous optimization, When models meet data, Linear regression) (Day 48) KAIST's AI503 Mathematics for AI (Matrix Decompositions) gnn (39) (Day 300) Trying out Neo4j's GraphRAG package (Day 279) Neo4j Graph Data Science Certification - SUCCESS! (Day 278) Before Machine Learning Volume 2 - Calculus + Neo4j GDS (Day 277) Neo4j Professional Certificate attempt 1 (Day 276) 4.5hr stream learning about intermediate neo4j queries (Day 274) Finished the Graph Algorithms for Data Science book + stream (Day 273) First stream on youtube and finishing chapter 9 of the graph algs book (Day 272) A bit more of Graph Algorithms for Data Science (Day 271) DE - insight and advice from industry experts (Day 232) Creating a script for the technical part of the KB project (Day 209) Using Mage for pipeline orchestration in the KB project (Day 202) Setting up a Graph Convolution Network model to detect fraud credit card transactions (Day 201) Struggling with neo4j and a fraud GNN (Day 200) Kukmin Bank AI competition project idea (Day 158) 50 minutes of audio in the Scottish dataset + exploring Mixture Density Networks in GNNs (Day 156) Final XCS224W - ML with Graphs homework (Day 142) Stanford's XCS224W - ML with Graphs - assignment 4 completed (Day 119) Graph Convolutional Transformer application on electronic health records (Day 115) Exploring HuggingFace's capabilities and submitting 3rd homework from the ML with Graphs course (Day 114) Trustworthy Graph AI (Day 113) Making a better blog + Geometric Graph Learning for Biology (Day 111) Advanced Topics in GNNs (Day 110) Learning about Graph Transformers (Day 109) Graph Generative Models (Day 108) Recommender systems + small adjustment to the text2chart webapp (Day 106) Community structure in networks (Day 105) Network subgraph counting and matching (Day 104) Reasoning over Knowledge Graphs (Day 103) Knowledge Graphs (Day 102) Label propagation in ML with Graphs (Day 99) XCS224W - ML with Graphs - Theory of GNNs (Day 98) Finishing XCS224W - ML with Graphs' 2nd homework on GNNs Using PyTorch Geometric (Day 97) Review of the GNN structure and training (last 2 days) + starting Colab 2 of XCS224W - ML with Graphs (Day 96) GNN Training Pipeline + looking for opportunities this summer (Day 95) Designing a GNN layer + becoming a fellow of the Royal Statistical Society (Day 94) Link analysis page rank random walks + First assignment + Short intro to GNNs (Day 93) Node embeddings in graphs + some foundational statistics/math (Day 92) Starting the official Stanford XCS224W - ML with Graphs (Day 90) Probability - Univariate Models and colab 0 from XCS224W - ML with Graphs cloud (24) (Day 198) Transactions Data Streaming Pipeline Porject (v1 completed) (Day 193) Chapter 5, 6, and 7 from Effective Data Science Infrastructure (Day 192) Chapter 4 - Scaling with the compute layer (from the book - Effective Data Science Infrastructure) (Day 189) I finished the Car Insurance Fraud MLOps project. Thank you MLOps zoomcamp for teaching me so much! (Day 186) Prefect cloud, model serving with FastAPI, and SHAP values (Day 184) Mlflow experiment tracking and trying out metaflow (Day 183) Failing to install Kubeflow, and setting up mlflow on GCP (Day 181) Lending club data engineering project - Done (Day 180) From Kaggle to BigQuery dimension tables - an end2end pipeline (Day 179) Using Docker, Makefile, and starting Data modelling for my Lending club project (Day 178) Starting 'Lending club data engineering project' (Day 176) Testing, Documentation, Deployment with dbt and visualisations with Looker (Day 175) Learning about and using dbt cloud (Day 174) Starting LLM zoomcamp + Learning about Data Warehouses + BigQuery (Day 173) Terraform, GCP, virtual machines, data pipelines (Day 172) Learning about terraform + adding more data to the Glaswegian audio dataset (Day 170) Uber data engineering project using GCP and Mage (Day 148) Microsoft Azure hackathon Day 2 (Day 147) Microsoft Azure hackathon Day 1 (Day 146)] MLOps zoomcamp module 2 homework + some more prep for Microsoft x NVIDIA's hackaton (Day 145) Build & Modernize AI Applications with Azure (prep for Microsoft Azure x NVIDIA hackaton in Seoul) (Day 137) AWS Summit Seoul Day 2 (Day 136) AWS Summit Seoul Day 1 (Day 101) Doing the Google Cloud Digital Leader Learning Path glaswegian (11) (Day 212) Final Glaswegian TTS model (Day 211) 2 hour mark !!! Glaswegian dataset goal - accomplished! + whisper-small fine-tuned (Day 210) 118 minutes of Glaswegian accent audio clips (Day 208) Setting up docker-services for the KB project, streaming transactions, and the Scottish dataset (Day 199) Continuing with Build an LLM from scratch (Day 187) Setting up postgres, pgAdmin, Grafana and FastAPI to run in Docker (Day 172) Learning about terraform + adding more data to the Glaswegian audio dataset (Day 158) 50 minutes of audio in the Scottish dataset + exploring Mixture Density Networks in GNNs (Day 151) Reading more about taxi OD matrix prediction architectures + more Scottish dataset audio included (Day 150) Learning more about taxi OD matrix prediction + Scottish dataset update (Day 138) Fine-tuning Speech T5 using a very small Glaswegian dataset data-eng (55) (Day 325) Dimension modelling (Day 324) I secured a free spot at Zach Wilson's Jan 2025 data engineering bootcamp (Day 323) Predicting subway demand + Additive Dimensions (Day 322) Data Eng camp homework 1 completed (maybe) (Day 321) Slowly changing dimensions (Day 320) Day 1 of Zach Wilson's DE bootcamp - Data Modelling (Day 317) Streaming + more of Chip Huyen's book (Day 316) Reading more of the infamous Designing ML systems book (Day 315) Finally started reading Chip Huyen's Designing ML systems (Day 304) LLMs + some dbt (Day 303) Using my professor's A6000 GPU for LLM fine-tuning (Day 302) Data engineering pipeline from the LLM Engineer's handbook (Day 301) LLM Engineer's Handbook (Day 299) ML in PySpark (Day 298) From Pandas to PySpark (Day 297) More about LLMs, and credit risk modelling (Day 290) Finishing the book - Financial Data Engineering (Day 281) Read chapter 4 of Andriy Burkov's MLE book (Day 280) Reading scikit-learn docs on stream + reading Andryi Burkov's MLE book (Day 271) DE - insight and advice from industry experts (Day 270) DE course by Joe Reis - completed (Day 269) The math behind neural nets + trying the capstone project from DL.AI's DE specialisation (Day 267) Continuing with the DE course by Joe Reis (Day 265) Day 6 of the DeepLearning.AI Data Engineering Professional Certificate course (Day 264) Day 5 of the DeepLearning.AI Data Engineering Professional Certificate course (Day 263) Day 4 of the DeepLearning.AI Data Engineering Professional Certificate course (Day 262) Day 3 of the DeepLearning.AI Data Engineering Professional Certificate course (Day 261) Day 2 of the DeepLearning.AI Data Engineering Professional Certificate course (Day 260) DeepLearning.AI Data Engineering Professional Certificate got realeased (Day 246) First deployment on Kubernetes (Day 245) Streaming dbs + EvidentlyAI course (Day 244) Streaming databases book + advanced RAG techniques (Day 243) Finishing a PySpark book (Day 242) PySpark day (Day 225) Starting the Finance Voice Assistant project (Day 224) Learning about Snowflake and starting the book - Deep Learning at Scale (Day 223) Finishing up Introducing MLOps (Day 222) Fundamentals of Data Engineering and Introducing MLOps in O'Reilly (Day 220) Chapter 2 The Data Engineering Lifecycle (Day 219) Fundamentals of Data Eng and LLM data preprocessing pipelines in Mage (Day 214) The evaluation of my MLOps zoomcamp project arrived - max points (Day 198) Transactions Data Streaming Pipeline Porject (v1 completed) (Day 197) Learning about Kafka (Day 181) Lending club data engineering project - Done (Day 180) From Kaggle to BigQuery dimension tables - an end2end pipeline (Day 179) Using Docker, Makefile, and starting Data modelling for my Lending club project (Day 178) Starting 'Lending club data engineering project' (Day 177) Spark for batch processing (Day 176) Testing, Documentation, Deployment with dbt and visualisations with Looker (Day 175) Learning about and using dbt cloud (Day 174) Starting LLM zoomcamp + Learning about Data Warehouses + BigQuery (Day 173) Terraform, GCP, virtual machines, data pipelines (Day 172) Learning about terraform + adding more data to the Glaswegian audio dataset (Day 171) Data engineering zoomcamp by DataTalksClub (Day 170) Uber data engineering project using GCP and Mage mathematics (1) (Day 291) Starting Machine Learning for Financial Risk Management with Python + some math for ML traditiona-machine-learning (4) (Day 304) LLMs + some dbt (Day 303) Using my professor's A6000 GPU for LLM fine-tuning (Day 302) Data engineering pipeline from the LLM Engineer's handbook (Day 301) LLM Engineer's Handbook mlop (1) (Day 321) Slowly changing dimensions
(Day 91) Probability - Multivariate models and Statistics chapters from Probabilistic Machine Learning
(Day 311) Checking out NODES '24, 10th chapter of LLM engineer's handbook, and linear models in sklearn
(Day 236) Reading about unsupervised learning algorithms + making the *final* version of our ppt videos
(Day 182) Learning about feature selection in fraud detection and finding a classifier model with low recall
(Day 311) Checking out NODES '24, 10th chapter of LLM engineer's handbook, and linear models in sklearn
(Day 236) Reading about unsupervised learning algorithms + making the *final* version of our ppt videos
(Day 235) Re-recording the real-time pipeline video and getting final feedback on our ppt for the KB project
(Day 216) Pipelines for XGBoost and CatBoost training, and using the models in the real-time inference pipeline
(Day 208) Setting up docker-services for the KB project, streaming transactions, and the Scottish dataset
(Day 192) Chapter 4 - Scaling with the compute layer (from the book - Effective Data Science Infrastructure)
(Day 189) I finished the Car Insurance Fraud MLOps project. Thank you MLOps zoomcamp for teaching me so much!
(Day 182) Learning about feature selection in fraud detection and finding a classifier model with low recall
(Day 160) Simple data engineering pipeline with Prefect, and... MLOps with mage.ai (tons of problems)
(Day 151) Reading more about taxi OD matrix prediction architectures + more Scottish dataset audio included
(Day 145) Build & Modernize AI Applications with Azure (prep for Microsoft Azure x NVIDIA hackaton in Seoul)
(Day 133) Gathering data for the Scottish dataset project + Factor analysis + Grokking ML + MLxFundamentals Day 4 (2)
(Day 115) Exploring HuggingFace's capabilities and submitting 3rd homework from the ML with Graphs course
(Day 100) Embeddings in practice + reading a couple of research papers + trying to deploy an LLM in production
(Day 97) Review of the GNN structure and training (last 2 days) + starting Colab 2 of XCS224W - ML with Graphs
(Day 87) Registered for Stanford's XCS224W - Machine Learning with Graphs + RAG webapp with llama-index tutorial
(Day 86) Made a youtube video - Chat with your PDF for free in colab using huggingface, mongodb, llama_index, langchain
(Day 58) Stanford CS224N (NLP with DL) Lecture 2 - Neural classifiers (diving deeper into word embeddings)
(Day 39) Reading papers of powerful CNN models + going back to basics (+ some more Andrej Karpathy lectures)
(Day 133) Gathering data for the Scottish dataset project + Factor analysis + Grokking ML + MLxFundamentals Day 4 (2)
(Day 115) Exploring HuggingFace's capabilities and submitting 3rd homework from the ML with Graphs course
(Day 100) Embeddings in practice + reading a couple of research papers + trying to deploy an LLM in production
(Day 87) Registered for Stanford's XCS224W - Machine Learning with Graphs + RAG webapp with llama-index tutorial
(Day 86) Made a youtube video - Chat with your PDF for free in colab using huggingface, mongodb, llama_index, langchain
(Day 84) Lecture 13 and 14 of CMU 11-711's Advanced NLP - Debugging and model interpretation; Ensembling methods
(Day 77) Review of the ACL 2023 talk, and lecture 10 from CMU's advanced NLP course about retrieval models
(Day 76) Finishing the Retrieval-based LM talk, and learning about distillation, quantization and pruning
(Day 64) Stanford CS224N (NLP with DL) - Coreference resolution, Adding knowledge to LMs, Code generation
(Day 155) Reading more about 'historic' (used as baseline) models for spatio-temporal predictions using graphs
(Day 151) Reading more about taxi OD matrix prediction architectures + more Scottish dataset audio included
(Day 149) Learning about the Origin-Destination Matrix Prediction problem in passenger prediction tasks
(Day 100) Embeddings in practice + reading a couple of research papers + trying to deploy an LLM in production
(Day 58) Stanford CS224N (NLP with DL) Lecture 2 - Neural classifiers (diving deeper into word embeddings)
(Day 39) Reading papers of powerful CNN models + going back to basics (+ some more Andrej Karpathy lectures)
(Day 235) Re-recording the real-time pipeline video and getting final feedback on our ppt for the KB project
(Day 216) Pipelines for XGBoost and CatBoost training, and using the models in the real-time inference pipeline
(Day 208) Setting up docker-services for the KB project, streaming transactions, and the Scottish dataset
(Day 192) Chapter 4 - Scaling with the compute layer (from the book - Effective Data Science Infrastructure)
(Day 189) I finished the Car Insurance Fraud MLOps project. Thank you MLOps zoomcamp for teaching me so much!
(Day 160) Simple data engineering pipeline with Prefect, and... MLOps with mage.ai (tons of problems)
(Day 311) Checking out NODES '24, 10th chapter of LLM engineer's handbook, and linear models in sklearn
(Day 236) Reading about unsupervised learning algorithms + making the *final* version of our ppt videos
(Day 192) Chapter 4 - Scaling with the compute layer (from the book - Effective Data Science Infrastructure)
(Day 145) Build & Modernize AI Applications with Azure (prep for Microsoft Azure x NVIDIA hackaton in Seoul)
(Day 133) Gathering data for the Scottish dataset project + Factor analysis + Grokking ML + MLxFundamentals Day 4 (2)
(Day 97) Review of the GNN structure and training (last 2 days) + starting Colab 2 of XCS224W - ML with Graphs
(Day 91) Probability - Multivariate models and Statistics chapters from Probabilistic Machine Learning
(Day 87) Registered for Stanford's XCS224W - Machine Learning with Graphs + RAG webapp with llama-index tutorial
(Day 84) Lecture 13 and 14 of CMU 11-711's Advanced NLP - Debugging and model interpretation; Ensembling methods
(Day 77) Review of the ACL 2023 talk, and lecture 10 from CMU's advanced NLP course about retrieval models
(Day 76) Finishing the Retrieval-based LM talk, and learning about distillation, quantization and pruning
(Day 64) Stanford CS224N (NLP with DL) - Coreference resolution, Adding knowledge to LMs, Code generation
(Day 58) Stanford CS224N (NLP with DL) Lecture 2 - Neural classifiers (diving deeper into word embeddings)
(Day 49) KAIST's AI503 Mathematics for AI (Continuous optimization, When models meet data, Linear regression)
(Day 49) KAIST's AI503 Mathematics for AI (Continuous optimization, When models meet data, Linear regression)
(Day 115) Exploring HuggingFace's capabilities and submitting 3rd homework from the ML with Graphs course
(Day 97) Review of the GNN structure and training (last 2 days) + starting Colab 2 of XCS224W - ML with Graphs
(Day 192) Chapter 4 - Scaling with the compute layer (from the book - Effective Data Science Infrastructure)
(Day 189) I finished the Car Insurance Fraud MLOps project. Thank you MLOps zoomcamp for teaching me so much!
(Day 145) Build & Modernize AI Applications with Azure (prep for Microsoft Azure x NVIDIA hackaton in Seoul)
(Day 208) Setting up docker-services for the KB project, streaming transactions, and the Scottish dataset
(Day 151) Reading more about taxi OD matrix prediction architectures + more Scottish dataset audio included
