-
Deep Learning the Series 102
-
DocumentLecture1.1
-
Week 1 – WelcomeLecture1.2
-
Week 1 – AI OverviewLecture1.3
-
Week 1 – IntroductionLecture1.4
-
Week 1 – Model CreationLecture1.5
-
Week 1 – ตอบคำถาม SSE มีไว้ใช้ทำอะไร?Lecture1.6
-
Week 1 – Data PreparationLecture1.7
-
Week 2 – Recap Week 1Lecture1.8
-
Week 2 – Model Evaluation for RegressionLecture1.9
-
Week 2 – Basic Workshop – Code PipelineLecture1.10
-
Week 2 – Basic Workshop – AI in MarketingLecture1.11
-
Week 2 – Basic Workshop – AI in InvestmentLecture1.12
-
Week 2 – Basic Workshop – Smart FarmLecture1.13
-
Week 2 – ตอบคำถามนักเรียนLecture1.14
-
Week 2 – Basic Workshop – AI in Rental BusinessLecture1.15
-
Week 2 – Basic Workshop – AI in InsuranceLecture1.16
-
Week 3 – Model Improvement – Strong AssumptionLecture1.17
-
Week 3 – Model Improvement – Weak AssumptionLecture1.18
-
Week 3 – Model Improvement – Problem with Linearly DependentLecture1.19
-
Week 3 – ตอบคำถาม ข้อเสียของการ drop one hotLecture1.20
-
Week 3 – Model Improvement – SolutionLecture1.21
-
Week 3 – Model Improvement – RegularizationLecture1.22
-
Week 3 – ตอบคำถาม RegularizationLecture1.23
-
Week 3 – Cross ValidationLecture1.24
-
Week 3 – Data Preparation – Feature ScalingLecture1.25
-
Week 3 – Advanced WorkshopLecture1.26
-
Week 4 – RoadmapLecture1.27
-
Week 4 – DL102 – IntroductionLecture1.28
-
Week 4 – LoR (2-class) – IntroductionLecture1.29
-
Week 4 – LoR (2-class) – DataLecture1.30
-
Week 4 – LoR (2-class) – Model Creation part1Lecture1.31
-
Week 4 – LoR (2-class) – Model Creation part2Lecture1.32
-
Week 4 – LoR (2-class) – PredictionLecture1.33
-
Week 4 – LoR (2-class) – ImprovementLecture1.34
-
Week 4 – Model Evaluation for ClassificationLecture1.35
-
Week 4 – Workshop LoR (2-class) – Code PipelineLecture1.36
-
Week 4 – Workshop LoR (2-class) – AI in HealthcareLecture1.37
-
Week 4 – Workshop LoR (2-class) – AI in AgricultureLecture1.38
-
Week 4 – Workshop LoR (2-class) – Fake NewsLecture1.39
-
week 5 – Recap – LoR (2-class)Lecture1.40
-
Week 5 – LoR (multi-class) – เกริ่นนำLecture1.41
-
Week 5 – LoR (multi-class) – Introduction – LoR with MulticlassLecture1.42
-
Week 5 – LoR (multi-class) – Introduction – Why Softmax?Lecture1.43
-
Week 5 – LoR (multi-class) – Introduction – Extension to Neural NetworkLecture1.44
-
Week 5 – LoR (multi-class) – Introduction – Real World ApplicationLecture1.45
-
Week 5 – ตอบคำถาม sigmoid vs softmaxLecture1.46
-
Week 5 – LoR (multi-class) – DataLecture1.47
-
Week – LoR (multi-class) – ModelLecture1.48
-
Week 5 – LoR (multi-class) – PredictionLecture1.49
-
Week 5 – LoR (multi-class) – ImprovementLecture1.50
-
Week 5 – Workshop LoR (multi-class) – AI in Fruit IndustryLecture1.51
-
Week 5 – Workshop LoR (multi-class) – AI in News CategorizationLecture1.52
-
Week 5 – Cross EntropyLecture1.53
-
Week 6 – RoadmapLecture1.54
-
Week 6 – DL103 – IntroductionLecture1.55
-
Week 6 – NN&DL – What is NN?Lecture1.56
-
Week 6 – NN&DL – Why we need NN?Lecture1.57
-
Week 6 – NN&DL – Real World ApplicationLecture1.58
-
Week 6 – NN&DL – Architecture of NNLecture1.59
-
Week 6 – NN&DL – Component of NNLecture1.60
-
Week 6 – NN&DL – How NN WorkLecture1.61
-
Week 6 – ตอบคำถาม decision boundary เปลี่ยนแปลงตามเวลาไหม?Lecture1.62
-
Week 6 – NN&DL – What is DL?Lecture1.63
-
Week 6 – NN&DL – Architecture of DLLecture1.64
-
Week 6 – NN&DL – Component of DLLecture1.65
-
Week 6 – NN&DL – Why we need DL?Lecture1.66
-
Week 6 – NN&DL – How DL WorkLecture1.67
-
Week 6 – DL for RegressionLecture1.68
-
Week 6 – DL for Binary ClassificationLecture1.69
-
Week 6 – DL for Multiclass ClassificationLecture1.70
-
Week 7 – Recap – NN&DLLecture1.71
-
Week 7 – DL Workshop – WorkflowLecture1.72
-
Week 7 – DL Workshop – OverviewLecture1.73
-
Week 7 – DL Workshop – Code Pipeline Part1Lecture1.74
-
Week 7 – DL Workshop – Code Pipeline Part2Lecture1.75
-
Week 7 – DL Workshop – Code Pipeline Part3Lecture1.76
-
Week 7 – DL Workshop – AI in Civil EngineeringLecture1.77
-
Week 7 – DL Workshop – AI in HealthcareLecture1.78
-
Week 7 – DL Workshop – AI in Skin CancerLecture1.79
-
Week 7 – ตอบคำถาม underfit & overfit คืออะไร?Lecture1.80
-
Week 7 – DL Interpretation – 1st Hidden LayerLecture1.81
-
Week 7 – DL Interpretation – 2nd Hidden LayerLecture1.82
-
Week 7 – DL Interpretation – 3rd Hidden Layer & ConclusionLecture1.83
-
Week 7 – DL Interpretation – Conclusion & AdaptationLecture1.84
-
Week 8 – Recap – DL InterpretationLecture1.85
-
Week 8 – Improvement of DL – OverviewLecture1.86
-
Week 8 – Speed Up with GPU – What is GPU?Lecture1.87
-
Week 8 – Speed Up with GPU – How GPU Accelerate DL?Lecture1.88
-
Week 8 – Speed Up with GPU – Welcome to ColabLecture1.89
-
Week 8 – Speed Up with GPU – Train Model with GPULecture1.90
-
Week 8 – Imbalanced ClassLecture1.91
-
Week 8 – L2, L1, Elastic NetLecture1.92
-
Week 8 – Dropout Regularization part1Lecture1.93
-
Week 8 – Dropout Regularization part2Lecture1.94
-
Week 8 – Gradient Descent VariantsLecture1.95
-
Week 8 – MomentumLecture1.96
-
Week 8 – Adagrad & RMSProp & AdamLecture1.97
-
Week 8 – Advanced Workshop – Code PipelineLecture1.98
-
Week 8 – Advanced Workshop – AI in Real Estate BusinessLecture1.99
-
Week 8 – Advanced Workshop – AI in Diagnosing Alzheimer’sLecture1.100
-
Week 8 – Advanced Workshop – AI in Speech RecognitionLecture1.101
-
Week 8 – สรุปคอร์ส DL the SeriesLecture1.102
-
-
Machine Learning 71
-
DocumentLecture2.1
-
Week 1 – IntroductionLecture2.2
-
Week 1 – K Nearest Neighbor : Theory : What is K Nearest Neighbor ?Lecture2.3
-
Week 1 – K Nearest Neighbor : Theory : StateLecture2.4
-
Week 1 – K Nearest Neighbor : Theory : How to create modelLecture2.5
-
Week 1 – K Nearest Neighbor : Theory : ตอบคำถามนักเรียนLecture2.6
-
Week 1 – K Nearest Neighbor : Theory : ตัวอย่างการคำนวณ KNN-CLecture2.7
-
Week 1 – K Nearest Neighbor : Theory : When will we use KNN-C ?Lecture2.8
-
Week 1 – K Nearest Neighbor : Code : Overview & Import LibraryLecture2.9
-
Week 1 – K Nearest Neighbor : Code : NormalizationLecture2.10
-
Week 1 – K Nearest Neighbor : Code : วัดระยะห่างLecture2.11
-
Week 1 – K Nearest Neighbor : Code : เรียงลำดับ Target ตามระยะห่างLecture2.12
-
Week 1 – K Nearest Neighbor : Code : หาผลลัพธ์จากเพื่อนบ้าน K ตัวLecture2.13
-
Week 1 – K Nearest Neighbor : Code : หา K ที่ดีที่สุดLecture2.14
-
Week 1 – K Nearest Neighbor : Code : เรียนรู้ & พยากรณ์ Part 1Lecture2.15
-
Week 1 – K Nearest Neighbor : Code : เรียนรู้ & พยากรณ์ Part 2Lecture2.16
-
Week 1 – K Nearest Neighbor for Regression : Theory & Code & ExampleLecture2.17
-
Week 1 – K Nearest Neighbor for Regression : Theory : เฉลยการบ้านLecture2.18
-
Week 1 – เฉลยการบ้าน K Nearest Neighbor for RegressionLecture2.19
-
Week 2 – Naive Bayes : Theory : Bayes’ TheoremLecture2.20
-
Week 2 – Naive Bayes : Theory : What is Naive Bayes ?Lecture2.21
-
Week 2 – Naive Bayes : Theory : StateLecture2.22
-
Week 2 – Naive Bayes : Theory : How to create modelLecture2.23
-
Week 2 – Naive Bayes : Theory : ตัวอย่างการคำนวณ Naive BayesLecture2.24
-
Week 2 – Naive Bayes : Theory : ที่มาสมการ Naive BayesLecture2.25
-
Week 2 – Naive Bayes : Code : Overview & Import LibraryLecture2.26
-
Week 2 – Naive Bayes : Code : สร้าง Dictionary สำหรับเตรียมนับสมาชิกและเตรียมเก็บความน่าจะเป็นLecture2.27
-
Week 2 – Naive Bayes : Code : นับจำนวนสมาชิกจาก Training Set เพื่อใส่ใน Count_MatrixLecture2.28
-
Week 2 – Naive Bayes : Code : คำนวณความน่าจะเป็นจาก Count_Matrix เพื่อใส่ใน Prob_Matrix & เรียนรู้Lecture2.29
-
Week 2 – Naive Bayes : Code : พยากรณ์Lecture2.30
-
Week 2 – Naive Bayes : Code : Read Data & Prepare Data, Create Model, Making PredictionsLecture2.31
-
Week 2 – Naive Bayes : ExampleLecture2.32
-
Week 2 – Naive Bayes : TheoryLecture2.33
-
Week 2 – Naive Bayes : ตัวอย่างการคำนวณ Naive Bayes Continuous DataLecture2.34
-
Week 2 – Naive Bayes : CodeLecture2.35
-
Week 2 – Naive Bayes : ExampleLecture2.36
-
Week 3 – Decision Tree : Theory : IntroductionLecture2.37
-
Week 3 – Decision Tree : Theory : What is Decision Tree ?Lecture2.38
-
Week 3 – Decision Tree : Theory : StateLecture2.39
-
Week 3 – Decision Tree : Theory : How to create modelLecture2.40
-
Week 3 – Decision Tree : Theory : ตัวอย่างการคำนวณ Decision TreeLecture2.41
-
Week 3 – Decision Tree : Theory : ที่มาของ GiniLecture2.42
-
Week 3 – Decision Tree : ตัวอย่างการเขียน Code RecursiveLecture2.43
-
Week 3 – Decision Tree : Code Part 1Lecture2.44
-
Week 3 – Decision Tree : Code Part 2Lecture2.45
-
Week 3 – Random ForestLecture2.46
-
Week 4 – SVM : Theory : IntroductionLecture2.47
-
Week 4 – SVM : Theory : Method of Lagrange MultipliersLecture2.48
-
Week 4 – SVM : Theory : ตัวอย่างการคำนวณ Method of Lagrange Multipliers Part 1Lecture2.49
-
Week 4 – SVM : Theory : ตัวอย่างการคำนวณ Method of Lagrange Multipliers Part 2Lecture2.50
-
Week 4 – SVM : Theory : Karush Kuhn TuckerLecture2.51
-
Week 4 – SVM : Theory : ตัวอย่างการคำนวณ Karush Kuhn Tucker Part 1Lecture2.52
-
Week 4 – SVM : Theory : ตัวอย่างการคำนวณ Karush Kuhn Tucker Part 2Lecture2.53
-
Week 4 – SVM : Theory : เฉลยการบ้าน Method of Lagrange Multipliers ข้อ 1Lecture2.54
-
Week 4 – SVM : Theory : เฉลยการบ้าน Karush Kuhn Tucker ข้อ 2Lecture2.55
-
Week 4 – SVM : Theory : What is SVM ? & StateLecture2.56
-
Week 4 – SVM : Theory : How to create model Part 1Lecture2.57
-
Week 4 – SVM : Theory : How to create model Part 2Lecture2.58
-
Week 4 – SVM : Theory : How to create model Part 3Lecture2.59
-
Week 4 – SVM : Theory : How to create model Part 4Lecture2.60
-
Week 4 – SVM : Theory : How to create model Part 5Lecture2.61
-
Week 4 – SVM : Theory : SMOLecture2.62
-
Week 4 – SVM : Theory : ตัวอย่างการคำนวณ SMO Part 1Lecture2.63
-
Week 4 – SVM : Theory : ตัวอย่างการคำนวณ SMO Part 2Lecture2.64
-
Week 4 – SVM : Code & ExampleLecture2.65
-
Week 5 – สรุปเนื้อหาคอร์ส Part 1Lecture2.66
-
Week 5 – สรุปเนื้อหาคอร์ส Part 2Lecture2.67
-
Week 5 – Workshop 1Lecture2.68
-
Week 5 – Workshop 2 Part 1Lecture2.69
-
Week 5 – Workshop 2 Part 2Lecture2.70
-
Week 5 – ปิดคอร์ส Machine LearningLecture2.71
-
-
Reinforcement Learning 12
-
DocumentLecture3.1
-
Week 1 – Introduction + Multi-Armed Bandit Problem part1Lecture3.2
-
Week 1 – Multi-Armed Bandit Problem part2 + Markov Decision Process part1Lecture3.3
-
Week 2 – Markov Decision Process part2Lecture3.4
-
Week 3 – Markov Decision Process part3Lecture3.5
-
Week 3 – Dynamic ProgrammingLecture3.6
-
Week 4 – Monte CarloLecture3.7
-
Week 4 – Temporal Difference part1Lecture3.8
-
Week 5 – Temporal Difference part2Lecture3.9
-
Week 5 – n-step Temporal DifferenceLecture3.10
-
Week 5 – Off-Policy + Roll OutLecture3.11
-
Week 6 – Code of Reinforcement LearningLecture3.12
-
-
Deep Reinforcement Learning 28
-
Document – Deep RLLecture4.1
-
Introduction to Deep RLLecture4.2
-
Fundamentals of RL – Core ConceptsLecture4.3
-
Fundamentals of RL – Markov Decision ProcessLecture4.4
-
Fundamentals of RL – Episodic and Continuing TasksLecture4.5
-
Fundamentals of RL – The Reward HypothesisLecture4.6
-
Fundamentals of RL – Discounted ReturnLecture4.7
-
RL Algorithms – PolicyLecture4.8
-
RL Algorithms – State-Value FunctionLecture4.9
-
RL Algorithms – Action-Value FunctionLecture4.10
-
RL Algorithms – RL AlgorithmsLecture4.11
-
Q-learning – Introduction to Q-LearningLecture4.12
-
Q-learning – Epsilon GreedyLecture4.13
-
Q-learning – Q-Table ImprovementLecture4.14
-
Q-learning – Q-Learning ExamplesLecture4.15
-
Deep Q-Networks – Introduction to DQNLecture4.16
-
Deep Q-Networks – Deep LearningLecture4.17
-
Deep Q-Networks – Deep Q NetworkLecture4.18
-
Deep Q-Networks – Fixed Q-TargetsLecture4.19
-
Deep Q-Networks – Experience ReplayLecture4.20
-
Deep Q-Networks – DQN AlgorithmsLecture4.21
-
DQN Code – Breakout EnvironmentLecture4.22
-
DQN Code – Import Libraries and Environment SetupLecture4.23
-
DQN Code – DQN ImplementationLecture4.24
-
DQN Code – Training DQNLecture4.25
-
DQN Code – Update DQNLecture4.26
-
DQN Code – Step-by-Step WalkthroughLecture4.27
-
DQN Code – Code ExecutionLecture4.28
-
-
Probability for Financial Machine Learning 65
-
DocumentLecture5.1
-
Week 1 – IntroductionLecture5.2
-
Week 1 – Basic Knowledge – Set and Set OperationsLecture5.3
-
Week 1 – Basic Knowledge – Functions and GraphingLecture5.4
-
Week 1 – Basic Knowledge – Sequences and SeriesLecture5.5
-
Week 1 – Basic Knowledge – Counting TechniqueLecture5.6
-
Week 2 – Probability – Sample Spaces and EventsLecture5.7
-
Week 2 – Probability – Properties of ProbabilityLecture5.8
-
Week 2 – Probability – Conditional ProbabilityLecture5.9
-
Week 2 – Probability – Bayes’ TheoremLecture5.10
-
Week 2 – Probability – Independent of EventLecture5.11
-
Week 3 – Random Variables – Discrete Random VariablesLecture5.12
-
Week 3 – Random Variables – Expectation and Variance of Discrete RandomLecture5.13
-
Week 3 – Random Variables – The Bernoulli DistributionLecture5.14
-
Week 3 – Random Variables – The Binomial DistributionLecture5.15
-
Week 3 – Random Variables – The Geometric DistributionLecture5.16
-
Week 3 – Random Variables – The Poisson DistributionLecture5.17
-
Week 3 – Random Variables – Continuous Random VariablesLecture5.18
-
Week 3 – Random Variables – Expectation and Variance of Continuous RandomLecture5.19
-
Week 3 – Random Variables – The Uniform DistributionLecture5.20
-
Week 3 – Random Variables – Random Number Generation by Rejection Method and Inversion MethodLecture5.21
-
Week 3 – Random Variables – The Normal DistributionLecture5.22
-
Week 3 – Random Variables – The Exponential DistributionLecture5.23
-
Week 3 – Random Variables – The Poisson ProcessLecture5.24
-
Week 3 – Random Variables – Recap and OverviewLecture5.25
-
Week 3 – Random Variables – Random WalkLecture5.26
-
Week 3 – Random Variables – Joint DistributionsLecture5.27
-
Week 3 – Random Variables – Marginal DistributionsLecture5.28
-
Week 3 – Random Variables – Conditional DistributionLecture5.29
-
Week 3 – Random Variables – Independence of Random VariablesLecture5.30
-
Week 3 – Random Variables – Covariance and CorrelationLecture5.31
-
Week 4 – Random Sampling – Random SamplingLecture5.32
-
Week 4 – Random Sampling – Moment Generating FunctionLecture5.33
-
Week 4 – Random Sampling – Sampling DistributionsLecture5.34
-
Week 4 – Random Sampling – Central Limit TheoremLecture5.35
-
Week 4 – Random Sampling – Distribution of VarianceLecture5.36
-
Week 4 – Random Sampling – ConclusionLecture5.37
-
Week 5 – Estimation – Point EstimatorLecture5.38
-
Week 5 – Estimation – Unbiased EstimatorLecture5.39
-
Week 5 – Estimation – Consistent EstimatorLecture5.40
-
Week 5 – Estimation – Asymptotically Normal EstimatorLecture5.41
-
Week 5 – Estimation – Moment MethodLecture5.42
-
Week 5 – Estimation – Maximum Likelihood MethodLecture5.43
-
Week 5 – Estimation – Bayesian MethodLecture5.44
-
Week 5 – Estimation – Confident IntervalLecture5.45
-
Week 5 – Estimation – Sample SizeLecture5.46
-
Week 6 – Hypothesis Testing – Statistical hypothesesLecture5.47
-
Week 6 – Hypothesis Testing – Two tails testLecture5.48
-
Week 6 – Hypothesis Testing – One tails testLecture5.49
-
Week 6 – Hypothesis Testing – p-valueLecture5.50
-
Week 6 – Hypothesis Testing – Goodness-of-fit testLecture5.51
-
Week 6 – Hypothesis Testing – Jarque-Bera normality testLecture5.52
-
Week 6 – Hypothesis Testing – Test of Independence and homogeneityLecture5.53
-
Week 6 – Hypothesis Testing – Kolomogorov-Smirnov testLecture5.54
-
Week 6 – Hypothesis Testing – งานวิจัยที่มีรากฐานจาก ProbLecture5.55
-
Week 6 – Hypothesis Testing – Linear regressionLecture5.56
-
Week 6 – Hypothesis Testing – MLE of coefficientsLecture5.57
-
Week 6 – Hypothesis Testing – CorrelationLecture5.58
-
Week 6 – Hypothesis Testing – ExtensionLecture5.59
-
Week 6 – Hypothesis Testing – Nonprametric TestLecture5.60
-
Week 6 – Hypothesis Testing – Sign testLecture5.61
-
Week 6 – Hypothesis Testing – Wilcoxon sign-rank testLecture5.62
-
Week 6 – Hypothesis Testing – Mann-Whitney U-test and KrusKruskal-Wallis H-testLecture5.63
-
Week 6 – Hypothesis Testing – Run testLecture5.64
-
Week 6 – Hypothesis Testing – Conclusion of Probability for Financial Machine LearningLecture5.65
-
-
Time Series Analysis and Stochastic Process 11
-
DocumentLecture6.1
-
Week 1 – Introduction & Time Series ModelLecture6.2
-
Week 1 – Time Series ModelLecture6.3
-
Week 2 – Statistics for Time Series Model (1)Lecture6.4
-
Week 2 – Statistics for Time Series Model (2)Lecture6.5
-
Week 3 – Other Models (ARCH/ Bilinear/ Piecewise Linear/ Multivariate) & Stochastic ProcessLecture6.6
-
Week 3 – Stochastic ProcessLecture6.7
-
Week 4 – Stochastic Calculus (1)Lecture6.8
-
Week 4 – Stochastic Calculus (2)Lecture6.9
-
Week 5 – Stochastic Numerical Methods (1)Lecture6.10
-
Week 5 – Stochastic Numerical Methods (2)Lecture6.11
-
-
AI for Finance 26
-
DocumentLecture7.1
-
Week 1 – Artificial Intelligence for Quantitative tradingLecture7.2
-
Week 1 – Introduction to Quantitative tradingLecture7.3
-
Week 1 – Introduction to Artificial IntelligenceLecture7.4
-
Week 1 – Trader WorkflowLecture7.5
-
Week 1 – Trade Signal – Problem and SolutionLecture7.6
-
Week 2 – Supervised Learning WorkflowLecture7.7
-
Week 2 – Data Preparation WorkflowLecture7.8
-
Week 2 – EMA Crossover as TriggerLecture7.9
-
Week 2 – Bollinger Band Touching as TriggerLecture7.10
-
Week 2 – Candlestick Patterns as TriggerLecture7.11
-
Week 3 – Labelling by Trade SignalLecture7.12
-
Week 3 – Labelling by Triple Barrier MethodLecture7.13
-
Week 3 – Feature Selection ConceptLecture7.14
-
Week 3 – Hypothesis for Feature SelectionLecture7.15
-
Week 4 – Introduction to Decision TreeLecture7.16
-
Week 4 – State Data, Model and PredictionLecture7.17
-
Week 4 – Decision Tree ModellingLecture7.18
-
Week 4 – Gini Impurity and Information GainLecture7.19
-
Week 4 – Decision Tree ExamplesLecture7.20
-
Week 5 – Coding for Data PreparationLecture7.21
-
Week 5 – Coding for Model Creation and Model EvaluationLecture7.22
-
Week 5 – Result AnalysisLecture7.23
-
Week 5 – Non-Stationary Market DistributionLecture7.24
-
Week 5 – Advanced Feature ExtractionLecture7.25
-
Week 5 – Advanced Model SelectionLecture7.26
-
-
Recommendation System 14
-
DocumentLecture8.1
-
Week 1 – Introduction to Recommendation SystemLecture8.2
-
Week 1 – Motivation behind Recommendation SystemLecture8.3
-
Week 1 – Delve into Explicit RatingLecture8.4
-
Week 1 – Delve into Implicit RatingLecture8.5
-
Week 2 – Content Based overviewLecture8.6
-
Week 2 – Model Based and Hybrid overviewLecture8.7
-
Week 3 – Delve into Content Based (Similarity function)Lecture8.8
-
Week 3 – Similarity Calculation ExamplesLecture8.9
-
Week 3 – Delve into Model Based (Matrix Factorization)Lecture8.10
-
Week 3 – User Matrix, Item Matrix and Approximated Rating MatrixLecture8.11
-
Week 4 – Recommendation coding overviewLecture8.12
-
Week 4 – Recommendation coding with Small DatasetLecture8.13
-
Week 4 – Recommendation coding with Real World DatasetLecture8.14
-
-
Mathematics for Approximation 53
-
DocumentLecture9.1
-
Week 1 – เกริ่นนำภาพรวมวิชาLecture9.2
-
Week 1 – ฟังก์ชันที่สื่อโดย Neural NetworkLecture9.3
-
Week 1 – สัจพจน์ของ vector spaceLecture9.4
-
Week 1 – เข้าใจคำว่า spanLecture9.5
-
Week 1 – เข้าใจคำว่า linearly independentLecture9.6
-
Week 1 – การตรวจสอบ linearly independent ด้วย matrixLecture9.7
-
Week 1 – ทำความรู้จักเบื้องต้นกับ vector space of functionsLecture9.8
-
Week 1 – radial kernel และตัวอย่างต่าง ๆLecture9.9
-
Week 1 – เข้าใจลักษณะและนิยามของ radial basis functions RBFLecture9.10
-
Week 1 – ตัวอย่างการทำ Interpolation ด้วย RBFLecture9.11
-
Week 1 – Activity 1Lecture9.12
-
Week 1 – Activity 2Lecture9.13
-
Week 2 – เกริ่นนำ and Fact เบื้องต้นLecture9.14
-
Week 2 – สัจพจน์ของ inner product และนิยามของ normLecture9.15
-
Week 2 – ตัวอย่าง Euclidean space R^nLecture9.16
-
Week 2 – สมบัติเบื้องต้น และสมบัติใน R^2 และ R^3Lecture9.17
-
Week 2 – สมบัติของ norm และ Cauchy-Schwarz Inequality (Theorem 2)Lecture9.18
-
Week 2 – นิยามของ orthogonal และ orthonormalLecture9.19
-
Week 2 – ถ้า orthogonal แล้วจะได้ linearly independent (Theorem 4)Lecture9.20
-
Week 2 – ตัวอย่าง + ภาพประกอบ + ทำความเข้าใจเพิ่มเติมLecture9.21
-
Week 2 – การแยกองค์ประกอบเวกเตอร์ (Theorem 5 + Corollary 6)Lecture9.22
-
Week 2 – การหา projection (Theorem เสริมนอกชีท + Theorem 7)Lecture9.23
-
Week 2 – ทำแบบฝึกหัดข้อ 3. และ 4.Lecture9.24
-
Week 2 – inner product ของ functionsLecture9.25
-
Week 2 – orthogonal set ที่สำคัญ (ที่นำไปสู่ Fourier series)Lecture9.26
-
Week 2 – หา projection ด้วย Theorem 7 ได้ผลลัพธ์ออกมาเป็น Fourier series (Definition 9-11)Lecture9.27
-
Week 2 – Activity I + IILecture9.28
-
Week 3 – เกริ่นนำ/ supremum & infimumLecture9.29
-
Week 3 – นิยามลิมิตของลำดับLecture9.30
-
Week 3 – นิยามลิมิตของอนุกรมอนันต์Lecture9.31
-
Week 3 – นิยามฟังก์ชันต่อเนื่องและ intermediate value theoremLecture9.32
-
Week 3 – metric spaces พร้อมตัวอย่าง metric แบบต่าง ๆLecture9.33
-
Week 3 – นิยามลิมิตและความต่อเนื่องใน metric spaceLecture9.34
-
Week 3 – open sets และ closed setsLecture9.35
-
Week 3 – limit points และ closureLecture9.36
-
Week 3 – density (อธิบายละเอียดพร้อมตัวอย่าง)Lecture9.37
-
Week 3 – แนะนำ topology เล็กน้อยLecture9.38
-
Week 3 – เฉลยแบบฝึกหัด 3.(b) และอธิบายเพิ่มเติมLecture9.39
-
Week 3 – compact sets และสมบัติที่น่าสนใจLecture9.40
-
Week 3 – Activity ILecture9.41
-
Week 3 – Activity IILecture9.42
-
Week 4 – การลู่เข้าแบบรายจุด (pointwise convergence)Lecture9.43
-
Week 4 – การลู่เข้าแบบสม่ำเสมอ (uniform convergence)Lecture9.44
-
Week 4 – normed linear spaceLecture9.45
-
Week 4 – supremum normLecture9.46
-
Week 4 – B(X) and C(X) spacesLecture9.47
-
Week 4 – Weierstrass theoremLecture9.48
-
Week 4 – Universal Approximation Theorem (Part 1)Lecture9.49
-
Week 4 – ตอบคำถามเกี่ยวกับ activation functionLecture9.50
-
Week 4 – Exercise 1.(a)Lecture9.51
-
Week 4 – Riemann & Lebesgue integrationUniversal Approximation Theorem (Part 2)Lecture9.52
-
Week 4 – ตอบคำถามตอนท้ายLecture9.53
-
This content is protected, please login and enroll course to view this content!
Week 1 – Welcome
Next