64) T-Norm, S-Norm, and Fuzzy Complement
9:49
64) T-Norm, S-Norm, and Fuzzy Complement
63) Types of Membership Functions
11:10
63) Types of Membership Functions
62) Overview on Fuzzy Logic
10:34
62) Overview on Fuzzy Logic
61) What is Fuzzy Logic?
1:54
61) What is Fuzzy Logic?
60) Back Propagation
12:15
60) Back Propagation
59) Vector Calculus 6: Tensor Contraction for Chain Rule
6:27
59) Vector Calculus 6: Tensor Contraction for Chain Rule
58) Vector Calculus 5
4:02
58) Vector Calculus 5
57) Vector Calculus 4: Common Derivatives and Chain Rule Informal Method
8:47
57) Vector Calculus 4: Common Derivatives and Chain Rule Informal Method
56) Vector Calculus 3: Derivative of a Vector w.r.t Matrix
5:18
56) Vector Calculus 3: Derivative of a Vector w.r.t Matrix
55) Vector Calculus 2: Derivative of a Vector w.r.t Vector
7:51
55) Vector Calculus 2: Derivative of a Vector w.r.t Vector
54) Vector Calculus 1: Derivative of a Scalar w.r.t Vector and w.r.t Matrix
7:34
54) Vector Calculus 1: Derivative of a Scalar w.r.t Vector and w.r.t Matrix
53) Why Vector Calculus is needed in Neural Networks
3:11
53) Why Vector Calculus is needed in Neural Networks
52) Losses for Neural Networks
6:04
52) Losses for Neural Networks
51) Forward Propagation and Activation Functions
12:59
51) Forward Propagation and Activation Functions
50) Neural Networks are useless without Activation Functions
7:56
50) Neural Networks are useless without Activation Functions
49) Vectorizing for Neural Networks
6:30
49) Vectorizing for Neural Networks
48) Multinomial Regression
8:31
48) Multinomial Regression
47) Euclidean Distance Loss
6:01
47) Euclidean Distance Loss
46) SVM (Hinge) Loss for Multi-Class Classification
7:36
46) SVM (Hinge) Loss for Multi-Class Classification
45) Perceptron Loss for Multi-Class Classification
11:41
45) Perceptron Loss for Multi-Class Classification
44) Multi-Class Classification
5:17
44) Multi-Class Classification
43) Batch vs Mini Batch vs Online Training
9:59
43) Batch vs Mini Batch vs Online Training
42) Logistic Regression
14:29
42) Logistic Regression
40) (Optional Video) Implementation of Bipolar Perceptron Part2
11:27
40) (Optional Video) Implementation of Bipolar Perceptron Part2
41) (Optional Video) Parallel Computing for Vectorization
3:49
41) (Optional Video) Parallel Computing for Vectorization
39) (Optional Video) Implementation of Bipolar Perceptron Part1: Inefficient Method
7:12
39) (Optional Video) Implementation of Bipolar Perceptron Part1: Inefficient Method
38) Logistic Regression Loss (Log Likelihood Loss)
5:56
38) Logistic Regression Loss (Log Likelihood Loss)
37) Feature Engineering for Non-Linearly Separable Data
4:16
37) Feature Engineering for Non-Linearly Separable Data
36) Centralization and Normalization of Data
5:15
36) Centralization and Normalization of Data
35) Support Vector Machines (SVMs)
15:27
35) Support Vector Machines (SVMs)