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[XCS224N] Lecture 8 – Translation, Seq2Seq, Attention
[XCS224N] Lecture 7 – Vanishing Gradients and Fancy RNNs
[XCS224N] Lecture 6 – Language Models and RNNs
[XCS224N] Lecture 5 – Dependency Parsing
[XCS224N] Lecture 3 – Neural Networks
[XCS224N] Lecture 4 – Backpropagation
[XCS224N] Lecture 2 – Word Vectors and Word Senses
[XCS224N] Lecture 1 – Introduction and Word Vectors
[Sequential Models] week3. Sequence models & Attention mechanism
[Sequential Models] week2. Natural Language Processing & Word Embeddings
[Sequential Models] week1. Recurrent Neural Networks
[Convolutional Neural Networks] week3. Object detection
[Convolutional Neural Networks] week2. Deep convolutional models: case studies
[Convolutional Neural Networks] week1. Foundations of Convolutional Neural Networks
[Structuring Machine Learning Projects] week2. ML Strategy (2)
[Structuring Machine Learning Projects] week1. ML Strategy (1)
[Improving Deep Neural Networks] week2. Optimization algorithms
[Improving Deep Neural Networks] week1. Practical aspects of Deep Learning
[Neural Networks and Deep Learning] week4. Deep Neural Network
[Neural Networks and Deep Learning] week3. Shallow Neural Network
[Neural Networks and Deep Learning] week2. Neural Networks Basics
[Neural Networks and Deep Learning] week1. Introduction to deep learning
[Android Dev] 1.2 Connect to the Internet
[Android Dev] 1.3 RecyclerView
[Android Dev] 1.1 Create Project Sunshine
[OCaml MOOC] week6: MODULES AND DATA ABSTRACTION
[OCaml MOOC] week5: EXCEPTIONS, INPUT OUTPUT AND IMPERATIVE CONSTRUCTS
[OCaml MOOC] week4: HIGHER ORDER FUNCTIONS
[OCaml MOOC] week3: MORE ADVANCED DATA STRUCTURES
[OCaml MOOC] week2: BASIC DATA STRUCTURES
[Scala MOOC II] Lec4 - Timely Effects
[Scala MOOC II] Lec 3: Functions and State
[OCaml MOOC] week1: BASIC TYPES, DEFINITIONS AND FUNCTIONS
[Scala MOOC II] Lec2: Lazy Evaluation
[learning torch] 6. optim (optimization tools)
[learning torch] 5. nngraph (another way to construct nn)
[learning torch] 4. Criterion (loss function)
[learning torch] 3. Container (models)
[learning torch] 2. Module (layers)
[OCaml MOOC] week0: intro and overview
[Scala MOOC II] Lec1: For Expressions and Monads
[Scala MOOC I] Lec6: Collections
[Scala MOOC I] Lec4: Types and Pattern Matching
[Scala MOOC I] Lec3: Data and Abstraction
[Scala MOOC I] Lec2: Higher Order Functions
[Scala MOOC I] Lec1: Functions & Evaluation
[Scala MOOC I] Lec0: Getting Started
(DeepLearning MOOC) Lesson 4: Deep Models for Text and Sequences
(DeepLearning MOOC) Lesson 3: Convolutional Neural Networks
(DeepLearning MOOC) Lesson 2: Deep Neural Networks
(DeepLearning MOOC) Lesson 1: From Machine Learning to Deep Learning
[Algorithms II] Week 6-3 Intractability
[Algorithms II] Week 6-2 Linear Programming
[Algorithms II] Week 6-1 Reductions
[Algorithms II] Week 5-2 Data Compression
[Algorithms II] Week 5-1 Regular Expressions
[Algorithms II] Week 4-2 Substring Search
[Algorithms II] Week 4-1 Tries
[Algorithms II] Week 3-2 Radix Sorts
[Algorithms II] Week 3-1 Maximum Flow
[Algorithms II] Week 2-2 Shortest Paths
[Algorithms II] Week 2-1 Minimum Spanning Trees
[Algorithms II] Week 1-2 Directed Graphs
[Algorithms II] Week 1-1 Undirected Graphs
Approximate Retrieval(2): simHash
minHash: 一种快速approximate retrieval方法
lin-reg = max-likelihood: 贝叶斯视角看线性回归
[Algorithms I] Week 6 Hash Tables
[Algorithms I] Week 5-2 Geometric Applications of BSTs
[Algorithms I] Week 5-1 Balanced Search Trees
[Algorithms I] Week 4-2b Binary Search Trees
[Algorithms I] Week 4-2a Elementry Symbol Tables
[Algorithms I] Week 4-1 Priority Queue
[Algorithms I] Week 3-2 Quicksort
[Algorithms I] Week 3-1 Mergesort
[Algorithms I] Week 2-2 Elementary Sorts
[Algorithms I] Week 2-1 Stacks and Queues
[Algorithms I] Week1-Lab: Percolation
[Algorithms I] Week 1-2 Analysis of Algorithms
[Spark MOOC note] lab4. Predicting Movie Ratings
[Algorithms I] Week 1-1 Union-Find
[Spark MOOC note] Lec8. Exploratory Data Analysis and Machine Learning
[Spark MOOC note] Lec7. Data Quality
[Spark MOOC note] Lec6. Structured Data