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

Stanford ML 10 Large Scale Machine Learning


Gradient Descent with Large Datasets

Learning with Large Datasets

Plot and vs training set size to estimate the type of problem (high bias or variance)

Stochastic Gradient Descent

Use 1 example in each iteration

  1. Randomly shuffle dataset
  2. Repeat {
    for {
    \[\theta_j := \theta_j - \alpha (h_{\theta}(x^{(i)})-y^{(i)})x_j^{(i)}\]
    (for )

Mini-Batch Gradient Descent

Use b examples in each iteration
b: mini-batch size (2-100)
Can be vectorized


  1. Randomly shuffle dataset
  2. Repeat {
    for {
    \[\theta_j := \theta_j - \alpha \frac{1}{10}\sum_{k=i}^{i+9}(h_{\theta}(x^{(k)})-y^{(k)})x_j^{(k)}\]
    (for )

Stochastic Gradient Descent Convergence

During learning, compute before updating using .
Every 1000 iterations (say), plot averaged over the last 1000 examples processed by algorithm.
Can slowly decrease over time to improve convergence like:

Advanced Topics

Online Learning

Adapt to changing user perference


Repeat forever {
Get corresponding to user
Update using :
\[\theta_j := \theta_j - \alpha (h_{\theta}(x)-y)x_j \mbox{ for } (j=0,1,…,n)\]
Discard this example

Map Reduce and Data Parallelism

For each iteration, divide the training set into several portions; use different machines to calculate summation of functions for all portions, then sum the results to update the parameters on the central server.

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