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| [TGx]Downloaded from torrentgalaxy.to .txt |
585б |
| 1. Class Imbalance - Why Is Accuracy Not Always The Best Metric .mp4 |
44.30Мб |
| 1. Class Imbalance - Why Is Accuracy Not Always The Best Metric .srt |
9.52Кб |
| 1. Derivation Of Back Propagation - Part 1.mp4 |
51.12Мб |
| 1. Derivation Of Back Propagation - Part 1.srt |
7.98Кб |
| 1. Get My Other Courses !.html |
1.88Кб |
| 1. Gradient Descent With Momentum - Part 1.mp4 |
45.78Мб |
| 1. Gradient Descent With Momentum - Part 1.srt |
11.93Кб |
| 1. Introduction To Batch Normalization - Part 1.mp4 |
61.82Мб |
| 1. Introduction To Batch Normalization - Part 1.srt |
15.08Кб |
| 1. Introduction To Multi-Layer Perceptrons.mp4 |
97.61Мб |
| 1. Introduction To Multi-Layer Perceptrons.srt |
18.29Кб |
| 1. Introduction To The Classification Problem.mp4 |
38.31Мб |
| 1. Introduction To The Classification Problem.srt |
8.56Кб |
| 1. Promo Video.mp4 |
33.92Мб |
| 1. Promo Video.srt |
1.99Кб |
| 1. Regression, Overfitting, And Underfitting.mp4 |
76.96Мб |
| 1. Regression, Overfitting, And Underfitting.srt |
21.92Кб |
| 1. Source Of Those Lectures.html |
256б |
| 1. The Error Function.mp4 |
61.42Мб |
| 1. The Error Function.srt |
11.92Кб |
| 1. The Perceptron Learning Rule - Part 1.mp4 |
22.47Мб |
| 1. The Perceptron Learning Rule - Part 1.srt |
6.69Кб |
| 1. The Sigmoid And Bernoulli Distribution.mp4 |
45.10Мб |
| 1. The Sigmoid And Bernoulli Distribution.srt |
7.93Кб |
| 10. Changing Activation Functions - Tanh - Relu - LeakyRelu.mp4 |
83.41Мб |
| 10. Changing Activation Functions - Tanh - Relu - LeakyRelu.srt |
18.78Кб |
| 10. Types Of Machine Learning.mp4 |
54.94Мб |
| 10. Types Of Machine Learning.srt |
11.91Кб |
| 11. Solved Example (I) Single Layer Perceptron Designed Graphically.mp4 |
84.11Мб |
| 11. Solved Example (I) Single Layer Perceptron Designed Graphically.srt |
15.22Кб |
| 2.1 WrittenNotes.rar |
13.15Мб |
| 2. A Simple Glimpse Of Overfitting.mp4 |
26.15Мб |
| 2. A Simple Glimpse Of Overfitting.srt |
5.88Кб |
| 2. Derivation Of Back Propagation - Part 2.mp4 |
117.32Мб |
| 2. Derivation Of Back Propagation - Part 2.srt |
14.68Кб |
| 2. Gradient Descent With Momentum - Part 2.mp4 |
59.47Мб |
| 2. Gradient Descent With Momentum - Part 2.srt |
15.97Кб |
| 2. Introduction To Batch Normalization - Part 2.mp4 |
36.34Мб |
| 2. Introduction To Batch Normalization - Part 2.srt |
8.01Кб |
| 2. Introduction To Machine Learning.mp4 |
36.62Мб |
| 2. Introduction To Machine Learning.srt |
10.90Кб |
| 2. Introduction To Reglarization.mp4 |
69.84Мб |
| 2. Introduction To Reglarization.srt |
12.04Кб |
| 2. Maximum Likelihood Estimation - Quick Overview.mp4 |
47.21Мб |
| 2. Maximum Likelihood Estimation - Quick Overview.srt |
12.94Кб |
| 2. Precision - Recall , And F1 Score.mp4 |
38.81Мб |
| 2. Precision - Recall , And F1 Score.srt |
7.65Кб |
| 2. Solved Example (II) MLP Design Graphically.mp4 |
111.76Мб |
| 2. Solved Example (II) MLP Design Graphically.srt |
16.54Кб |
| 2. The Cross Entropy Cost Function - Derivation.mp4 |
73.42Мб |
| 2. The Cross Entropy Cost Function - Derivation.srt |
16.39Кб |
| 2. The Perceptron Learning Rule - Part 2.mp4 |
53.44Мб |
| 2. The Perceptron Learning Rule - Part 2.srt |
13.14Кб |
| 2. The Sigmoid Activation Function Again.mp4 |
53.77Мб |
| 2. The Sigmoid Activation Function Again.srt |
10.42Кб |
| 3. Adagrad And RMSProb.mp4 |
61.16Мб |
| 3. Adagrad And RMSProb.srt |
14.44Кб |
| 3. Derivation Of Back Propagation - Part 3.mp4 |
127.44Мб |
| 3. Derivation Of Back Propagation - Part 3.srt |
16.91Кб |
| 3. Deriving The Gradient Descent Algorithm.mp4 |
47.76Мб |
| 3. Deriving The Gradient Descent Algorithm.srt |
11.15Кб |
| 3. Different Ways For Regularization.mp4 |
35.73Мб |
| 3. Different Ways For Regularization.srt |
6.50Кб |
| 3. F1 Score vs Simple Average.mp4 |
26.62Мб |
| 3. F1 Score vs Simple Average.srt |
7.02Кб |
| 3. Forward Pass Equations For Batch Normalization.mp4 |
55.84Мб |
| 3. Forward Pass Equations For Batch Normalization.srt |
14.00Кб |
| 3. Intuition Of Multi-Layer Perceptrons - Part 1.mp4 |
69.82Мб |
| 3. Intuition Of Multi-Layer Perceptrons - Part 1.srt |
13.88Кб |
| 3. Maximum Likelihood Estimation Of Gaussian Distribution Parameters.mp4 |
60.00Мб |
| 3. Maximum Likelihood Estimation Of Gaussian Distribution Parameters.srt |
10.53Кб |
| 3. Proof Perceptron Convergence Theorem - Part 1.mp4 |
101.96Мб |
| 3. Proof Perceptron Convergence Theorem - Part 1.srt |
17.80Кб |
| 3. The Cross Entropy & The Vanishing Gradient Problem.mp4 |
45.63Мб |
| 3. The Cross Entropy & The Vanishing Gradient Problem.srt |
9.78Кб |
| 3. The Perceptron Equation.mp4 |
42.41Мб |
| 3. The Perceptron Equation.srt |
9.40Кб |
| 4. Adam And Learning Rate Decay.mp4 |
65.60Мб |
| 4. Adam And Learning Rate Decay.srt |
15.14Кб |
| 4. Batch Normalization Inference.mp4 |
57.62Мб |
| 4. Batch Normalization Inference.srt |
10.11Кб |
| 4. Cross Entropy In Multi-Class Problems.mp4 |
55.93Мб |
| 4. Cross Entropy In Multi-Class Problems.srt |
12.38Кб |
| 4. Intuition Of Multi-Layer Perceptrons - Part 2.mp4 |
68.05Мб |
| 4. Intuition Of Multi-Layer Perceptrons - Part 2.srt |
11.31Кб |
| 4. L1 vs L2 Regularization - Part 1 - Gradient Descent.mp4 |
42.44Мб |
| 4. L1 vs L2 Regularization - Part 1 - Gradient Descent.srt |
8.65Кб |
| 4. Notes About Gradient Descent.mp4 |
29.69Мб |
| 4. Notes About Gradient Descent.srt |
6.56Кб |
| 4. Precision-Recall Curve.mp4 |
38.50Мб |
| 4. Precision-Recall Curve.srt |
9.01Кб |
| 4. Proof Perceptron Convergence Theorem - Part 2.mp4 |
22.93Мб |
| 4. Proof Perceptron Convergence Theorem - Part 2.srt |
5.97Кб |
| 4. Vectorization Of BackPropagation - Part 1.mp4 |
40.05Мб |
| 4. Vectorization Of BackPropagation - Part 1.srt |
6.01Кб |
| 4. Visualization Of The Perceptron Equation.mp4 |
27.12Мб |
| 4. Visualization Of The Perceptron Equation.srt |
5.53Кб |
| 5. Derivation Of Back Propagation Through Batch Normalization - Part (I).mp4 |
77.41Мб |
| 5. Derivation Of Back Propagation Through Batch Normalization - Part (I).srt |
15.11Кб |
| 5. L1 vs L2 Regularization -Part 2 - Numerical, Intuitive, And Graphical Comparison.mp4 |
107.01Мб |
| 5. L1 vs L2 Regularization -Part 2 - Numerical, Intuitive, And Graphical Comparison.srt |
22.85Кб |
| 5. More Notes And filling Up.mp4 |
68.25Мб |
| 5. More Notes And filling Up.srt |
12.85Кб |
| 5. Proof Perceptron Convergence Theorem - Part 3.mp4 |
48.07Мб |
| 5. Proof Perceptron Convergence Theorem - Part 3.srt |
7.60Кб |
| 5. Proof Weight Vector Is Perpendicular To The Decision Boundary.mp4 |
57.60Мб |
| 5. Proof Weight Vector Is Perpendicular To The Decision Boundary.srt |
9.37Кб |
| 5. ROC and AUC.mp4 |
86.58Мб |
| 5. ROC and AUC.srt |
18.13Кб |
| 5. The Softmax Activation Function.mp4 |
27.38Мб |
| 5. The Softmax Activation Function.srt |
5.00Кб |
| 5. The Vanishing Gradient Problem.mp4 |
46.87Мб |
| 5. The Vanishing Gradient Problem.srt |
8.04Кб |
| 5. The XOR Problem - Part 1.mp4 |
93.19Мб |
| 5. The XOR Problem - Part 1.srt |
19.42Кб |
| 5. Vectorization Of BackPropagation - Part 2.mp4 |
56.54Мб |
| 5. Vectorization Of BackPropagation - Part 2.srt |
10.53Кб |
| 6. BackPropagation Derivation For The Softmax Activation Function.mp4 |
107.45Мб |
| 6. BackPropagation Derivation For The Softmax Activation Function.srt |
18.11Кб |
| 6. Derivation Of Back Propagation Though Batch Normalization - Part 2.mp4 |
110.40Мб |
| 6. Derivation Of Back Propagation Though Batch Normalization - Part 2.srt |
17.54Кб |
| 6. Dropout ! - Intuition.mp4 |
68.73Мб |
| 6. Dropout ! - Intuition.srt |
11.96Кб |
| 6. Input Centering And Normalization - Part 1.mp4 |
25.23Мб |
| 6. Input Centering And Normalization - Part 1.srt |
6.69Кб |
| 6. More Visualization For The Perceptron Weights - I.mp4 |
66.69Мб |
| 6. More Visualization For The Perceptron Weights - I.srt |
14.52Кб |
| 6. Solved Example (III) Gradient Descent Convergence.mp4 |
66.31Мб |
| 6. Solved Example (III) Gradient Descent Convergence.srt |
14.90Кб |
| 6. The XOR Problem - Part 2.mp4 |
33.33Мб |
| 6. The XOR Problem - Part 2.srt |
6.94Кб |
| 6. Three Main Problems Of The Threshold Perceptron.mp4 |
30.16Мб |
| 6. Three Main Problems Of The Threshold Perceptron.srt |
7.67Кб |
| 6. Vectorization Of BackPropagation - Part 3.mp4 |
35.90Мб |
| 6. Vectorization Of BackPropagation - Part 3.srt |
5.80Кб |
| 7. Dropout vs Inverted Dropout.mp4 |
47.80Мб |
| 7. Dropout vs Inverted Dropout.srt |
6.26Кб |
| 7. Input Centering And Normalization - Part 2.mp4 |
40.33Мб |
| 7. Input Centering And Normalization - Part 2.srt |
10.17Кб |
| 7. More Visualization Of The Perceptron Weights - II.mp4 |
114.75Мб |
| 7. More Visualization Of The Perceptron Weights - II.srt |
18.27Кб |
| 7. MultiClass Classification And The Sigmoid Activation.mp4 |
92.16Мб |
| 7. MultiClass Classification And The Sigmoid Activation.srt |
17.59Кб |
| 7. Notes About Softmax.html |
1.55Кб |
| 7. Solved Example (IIII) MLP With Linear Activations.mp4 |
26.41Мб |
| 7. Solved Example (IIII) MLP With Linear Activations.srt |
4.52Кб |
| 7. Vectorization Of BackPropagation - Part 4.mp4 |
20.10Мб |
| 7. Vectorization Of BackPropagation - Part 4.srt |
3.36Кб |
| 8. Activation Functions.mp4 |
18.00Мб |
| 8. Activation Functions.srt |
4.84Кб |
| 8. Dropout in a nutshell.html |
326б |
| 8. Vectorization Of BackPropagation - Part 5 - Batch Vectorization.mp4 |
76.11Мб |
| 8. Vectorization Of BackPropagation - Part 5 - Batch Vectorization.srt |
15.62Кб |
| 8. Vectorized Notation And The Weight Matrix.mp4 |
26.67Мб |
| 8. Vectorized Notation And The Weight Matrix.srt |
3.15Кб |
| 8. Weight Initialization - Part 1 - The Symmetry Problem.mp4 |
41.14Мб |
| 8. Weight Initialization - Part 1 - The Symmetry Problem.srt |
9.97Кб |
| 9. Cross-Validation How Do I Know I Am Overfitting Or Underfitting .mp4 |
50.68Мб |
| 9. Cross-Validation How Do I Know I Am Overfitting Or Underfitting .srt |
13.52Кб |
| 9. Graphical Representation Of A Neural Network.mp4 |
24.37Мб |
| 9. Graphical Representation Of A Neural Network.srt |
6.16Кб |
| 9. Weight Initialization - Part 2.mp4 |
42.08Мб |
| 9. Weight Initialization - Part 2.srt |
9.93Кб |
| TutsNode.com.txt |
63б |