Public speaking course notes
Read "Dynamo, Amazon’s Highly Available Key-value Store"
Read "Bigtable, A Distributed Storage System for Structured Data"
Read "Streaming Systems" 3, Watermarks
Read "Streaming Systems" 1&2, Streaming 101
Read "F1, a distributed SQL database that scales"
Read "Zanzibar, Google’s Consistent, Global Authorization System"
Read "Spanner, Google's Globally-Distributed Database"
Read "Designing Data-intensive applications" 12, The Future of Data Systems
IOS development with Swift
Read "Designing Data-intensive applications" 10&11, Batch and Stream Processing
Read "Designing Data-intensive applications" 9, Consistency and Consensus
Read "Designing Data-intensive applications" 8, Distributed System Troubles
Read "Designing Data-intensive applications" 7, Transactions
Read "Designing Data-intensive applications" 6, Partitioning
Read "Designing Data-intensive applications" 5, Replication
Read "Designing Data-intensive applications" 3&4, Storage, Retrieval, Encoding
Read "Designing Data-intensive applications" 1&2, Foundation of Data Systems
Three cases of binary search
TAMU Operating System 2 Memory Management
TAMU Operating System 1 Introduction
Overview in cloud computing 2
TAMU Operating System 7 Virtualization
TAMU Operating System 6 File System
TAMU Operating System 5 I/O and Disk Management
TAMU Operating System 4 Synchronization
TAMU Operating System 3 Concurrency and Threading
TAMU Computer Networks 5 Data Link Layer
TAMU Computer Networks 4 Network Layer
TAMU Computer Networks 3 Transport Layer
TAMU Computer Networks 2 Application Layer
TAMU Computer Networks 1 Introduction
Overview in distributed systems and cloud computing 1
A well-optimized Union-Find implementation, in Java
A heap implementation supporting deletion
TAMU Advanced Algorithms 3, Maximum Bandwidth Path (Dijkstra, MST, Linear)
TAMU Advanced Algorithms 2, B+ tree and Segment Intersection
TAMU Advanced Algorithms 1, BST, 2-3 Tree and Heap
TAMU AI, Searching problems
Factorization Machine and Field-aware Factorization Machine for CTR prediction
TAMU Neural Network 10 Information-Theoretic Models
TAMU Neural Network 9 Principal Component Analysis
TAMU Neural Network 8 Neurodynamics
TAMU Neural Network 7 Self-Organizing Maps
TAMU Neural Network 6 Deep Learning Overview
TAMU Neural Network 5 Radial-Basis Function Networks
TAMU Neural Network 4 Multi-Layer Perceptrons
TAMU Neural Network 3 Single-Layer Perceptrons
Princeton Algorithms P1W6 Hash Tables & Symbol Table Applications
Stanford ML 11 Application Example Photo OCR
Stanford ML 10 Large Scale Machine Learning
Stanford ML 9 Anomaly Detection and Recommender Systems
Stanford ML 8 Clustering & Principal Component Analysis
Princeton Algorithms P1W5 Balanced Search Trees
TAMU Neural Network 2 Learning Processes
TAMU Neural Network 1 Introduction
Stanford ML 7 Support Vector Machine
Stanford ML 6 Evaluate Algorithms
Princeton Algorithms P1W4 Priority Queues and Symbol Tables
Stanford ML 5 Neural Networks Learning
Princeton Algorithms P1W3 Mergesort and Quicksort
Stanford ML 4 Neural Networks Basics
Princeton Algorithms P1W2 Stack and Queue, Basic Sorts
Stanford ML 3 Classification Problems
Stanford ML 2 Multivariate Regression and Normal Equation
Princeton Algorithms P1W1 Union and Find
Stanford ML 1 Introduction and Parameter Learning