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Udemy - College Level Neural Nets [I] – Basic Nets: Math & Practice!

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Udemy - College Level Neural Nets [I] – Basic Nets: Math & Practice! (Size: 4.39 GB)
  TutsNode.com.txt 63 B
  [TGx]Downloaded from torrentgalaxy.to .txt 585 B
  [TutsNode.com] - College Level Neural Nets [I] - Basic Nets Math & Practice!
  1. Introduction To Machine Learning
  1. Promo Video.mp4 33.92 MB
  1. Promo Video.srt 1.99 KB
  2. Introduction To Machine Learning.mp4 36.62 MB
  2. Introduction To Machine Learning.srt 10.9 KB
  2.1 WrittenNotes.rar 13.15 MB
  10. Maximum Likelihood Estimation Review
  1. Source Of Those Lectures.html 256 B
  2. Maximum Likelihood Estimation - Quick Overview.mp4 47.21 MB
  2. Maximum Likelihood Estimation - Quick Overview.srt 12.94 KB
  3. Maximum Likelihood Estimation Of Gaussian Distribution Parameters.mp4 60 MB
  3. Maximum Likelihood Estimation Of Gaussian Distribution Parameters.srt 10.53 KB
  11. Improving Neural Network Performance - Part (II)
  1. The Sigmoid And Bernoulli Distribution.mp4 45.1 MB
  1. The Sigmoid And Bernoulli Distribution.srt 7.93 KB
  2. The Cross Entropy Cost Function - Derivation.mp4 73.42 MB
  2. The Cross Entropy Cost Function - Derivation.srt 16.39 KB
  3. The Cross Entropy & The Vanishing Gradient Problem.mp4 45.63 MB
  3. The Cross Entropy & The Vanishing Gradient Problem.srt 9.78 KB
  4. Cross Entropy In Multi-Class Problems.mp4 55.93 MB
  4. Cross Entropy In Multi-Class Problems.srt 12.38 KB
  5. The Softmax Activation Function.mp4 27.38 MB
  5. The Softmax Activation Function.srt 5 KB
  6. BackPropagation Derivation For The Softmax Activation Function.mp4 107.45 MB
  6. BackPropagation Derivation For The Softmax Activation Function.srt 18.11 KB
  7. Notes About Softmax.html 1.55 KB
  12. Batch Normalization !
  1. Introduction To Batch Normalization - Part 1.mp4 61.82 MB
  1. Introduction To Batch Normalization - Part 1.srt 15.08 KB
  2. Introduction To Batch Normalization - Part 2.mp4 36.34 MB
  2. Introduction To Batch Normalization - Part 2.srt 8.01 KB
  3. Forward Pass Equations For Batch Normalization.mp4 55.84 MB
  3. Forward Pass Equations For Batch Normalization.srt 14 KB
  4. Batch Normalization Inference.mp4 57.62 MB
  4. Batch Normalization Inference.srt 10.11 KB
  5. Derivation Of Back Propagation Through Batch Normalization - Part (I).mp4 77.41 MB
  5. Derivation Of Back Propagation Through Batch Normalization - Part (I).srt 15.11 KB
  6. Derivation Of Back Propagation Though Batch Normalization - Part 2.mp4 110.4 MB
  6. Derivation Of Back Propagation Though Batch Normalization - Part 2.srt 17.54 KB
  13. Get My Other Courses !
  1. Get My Other Courses !.html 1.88 KB
  2. The Linear Perceptron
  1. Introduction To The Classification Problem.mp4 38.31 MB
  1. Introduction To The Classification Problem.srt 8.56 KB
  10. Types Of Machine Learning.mp4 54.94 MB
  10. Types Of Machine Learning.srt 11.91 KB
  11. Solved Example (I) Single Layer Perceptron Designed Graphically.mp4 84.11 MB
  11. Solved Example (I) Single Layer Perceptron Designed Graphically.srt 15.22 KB
  2. A Simple Glimpse Of Overfitting.mp4 26.15 MB
  2. A Simple Glimpse Of Overfitting.srt 5.88 KB
  3. The Perceptron Equation.mp4 42.41 MB
  3. The Perceptron Equation.srt 9.4 KB
  4. Visualization Of The Perceptron Equation.mp4 27.12 MB
  4. Visualization Of The Perceptron Equation.srt 5.53 KB
  5. Proof Weight Vector Is Perpendicular To The Decision Boundary.mp4 57.6 MB
  5. Proof Weight Vector Is Perpendicular To The Decision Boundary.srt 9.37 KB
  6. More Visualization For The Perceptron Weights - I.mp4 66.69 MB
  6. More Visualization For The Perceptron Weights - I.srt 14.52 KB
  7. More Visualization Of The Perceptron Weights - II.mp4 114.75 MB
  7. More Visualization Of The Perceptron Weights - II.srt 18.27 KB
  8. Activation Functions.mp4 18 MB
  8. Activation Functions.srt 4.84 KB
  9. Graphical Representation Of A Neural Network.mp4 24.37 MB
  9. Graphical Representation Of A Neural Network.srt 6.16 KB
  3. Non-Linearly Separable Data And The Multi Layer Perceptron (MLP)
  1. Introduction To Multi-Layer Perceptrons.mp4 97.61 MB
  1. Introduction To Multi-Layer Perceptrons.srt 18.29 KB
  2. Solved Example (II) MLP Design Graphically.mp4 111.76 MB
  2. Solved Example (II) MLP Design Graphically.srt 16.54 KB
  3. Intuition Of Multi-Layer Perceptrons - Part 1.mp4 69.82 MB
  3. Intuition Of Multi-Layer Perceptrons - Part 1.srt 13.88 KB
  4. Intuition Of Multi-Layer Perceptrons - Part 2.mp4 68.05 MB
  4. Intuition Of Multi-Layer Perceptrons - Part 2.srt 11.31 KB
  5. The XOR Problem - Part 1.mp4 93.19 MB
  5. The XOR Problem - Part 1.srt 19.42 KB
  6. The XOR Problem - Part 2.mp4 33.33 MB
  6. The XOR Problem - Part 2.srt 6.94 KB
  7. MultiClass Classification And The Sigmoid Activation.mp4 92.16 MB
  7. MultiClass Classification And The Sigmoid Activation.srt 17.59 KB
  8. Vectorized Notation And The Weight Matrix.mp4 26.67 MB
  8. Vectorized Notation And The Weight Matrix.srt 3.15 KB
  4. Perceptron Learning !
  1. The Perceptron Learning Rule - Part 1.mp4 22.47 MB
  1. The Perceptron Learning Rule - Part 1.srt 6.69 KB
  2. The Perceptron Learning Rule - Part 2.mp4 53.44 MB
  2. The Perceptron Learning Rule - Part 2.srt 13.14 KB
  3. Proof Perceptron Convergence Theorem - Part 1.mp4 101.96 MB
  3. Proof Perceptron Convergence Theorem - Part 1.srt 17.8 KB
  4. Proof Perceptron Convergence Theorem - Part 2.mp4 22.93 MB
  4. Proof Perceptron Convergence Theorem - Part 2.srt 5.97 KB
  5. Proof Perceptron Convergence Theorem - Part 3.mp4 48.07 MB
  5. Proof Perceptron Convergence Theorem - Part 3.srt 7.6 KB
  6. Three Main Problems Of The Threshold Perceptron.mp4 30.16 MB
  6. Three Main Problems Of The Threshold Perceptron.srt 7.67 KB
  5. The Gradient Descent Algorithm
  1. The Error Function.mp4 61.42 MB
  1. The Error Function.srt 11.92 KB
  2. The Sigmoid Activation Function Again.mp4 53.77 MB
  2. The Sigmoid Activation Function Again.srt 10.42 KB
  3. Deriving The Gradient Descent Algorithm.mp4 47.76 MB
  3. Deriving The Gradient Descent Algorithm.srt 11.15 KB
  4. Notes About Gradient Descent.mp4 29.69 MB
  4. Notes About Gradient Descent.srt 6.56 KB
  5. More Notes And filling Up.mp4 68.25 MB
  5. More Notes And filling Up.srt 12.85 KB
  6. Solved Example (III) Gradient Descent Convergence.mp4 66.31 MB
  6. Solved Example (III) Gradient Descent Convergence.srt 14.9 KB
  7. Solved Example (IIII) MLP With Linear Activations.mp4 26.41 MB
  7. Solved Example (IIII) MLP With Linear Activations.srt 4.52 KB
  6. The Back-Propagation Algorithm !
  1. Derivation Of Back Propagation - Part 1.mp4 51.12 MB
  1. Derivation Of Back Propagation - Part 1.srt 7.98 KB
  2. Derivation Of Back Propagation - Part 2.mp4 117.32 MB
  2. Derivation Of Back Propagation - Part 2.srt 14.68 KB
  3. Derivation Of Back Propagation - Part 3.mp4 127.44 MB
  3. Derivation Of Back Propagation - Part 3.srt 16.91 KB
  4. Vectorization Of BackPropagation - Part 1.mp4 40.05 MB
  4. Vectorization Of BackPropagation - Part 1.srt 6.01 KB
  5. Vectorization Of BackPropagation - Part 2.mp4 56.54 MB
  5. Vectorization Of BackPropagation - Part 2.srt 10.53 KB
  6. Vectorization Of BackPropagation - Part 3.mp4 35.9 MB
  6. Vectorization Of BackPropagation - Part 3.srt 5.8 KB
  7. Vectorization Of BackPropagation - Part 4.mp4 20.1 MB
  7. Vectorization Of BackPropagation - Part 4.srt 3.36 KB
  8. Vectorization Of BackPropagation - Part 5 - Batch Vectorization.mp4 76.11 MB
  8. Vectorization Of BackPropagation - Part 5 - Batch Vectorization.srt 15.62 KB
  7. Regularization !
  1. Regression, Overfitting, And Underfitting.mp4 76.96 MB
  1. Regression, Overfitting, And Underfitting.srt 21.92 KB
  2. Introduction To Reglarization.mp4 69.84 MB
  2. Introduction To Reglarization.srt 12.04 KB
  3. Different Ways For Regularization.mp4 35.73 MB
  3. Different Ways For Regularization.srt 6.5 KB
  4. L1 vs L2 Regularization - Part 1 - Gradient Descent.mp4 42.44 MB
  4. L1 vs L2 Regularization - Part 1 - Gradient Descent.srt 8.65 KB
  5. L1 vs L2 Regularization -Part 2 - Numerical, Intuitive, And Graphical Comparison.mp4 107.01 MB
  5. L1 vs L2 Regularization -Part 2 - Numerical, Intuitive, And Graphical Comparison.srt 22.85 KB
  6. Dropout ! - Intuition.mp4 68.73 MB
  6. Dropout ! - Intuition.srt 11.96 KB
  7. Dropout vs Inverted Dropout.mp4 47.8 MB
  7. Dropout vs Inverted Dropout.srt 6.26 KB
  8. Dropout in a nutshell.html 326 B
  9. Cross-Validation How Do I Know I Am Overfitting Or Underfitting .mp4 50.68 MB
  9. Cross-Validation How Do I Know I Am Overfitting Or Underfitting .srt 13.52 KB
  8. Model Performance Metrics !
  1. Class Imbalance - Why Is Accuracy Not Always The Best Metric .mp4 44.3 MB
  1. Class Imbalance - Why Is Accuracy Not Always The Best Metric .srt 9.52 KB
  2. Precision - Recall , And F1 Score.mp4 38.81 MB
  2. Precision - Recall , And F1 Score.srt 7.65 KB
  3. F1 Score vs Simple Average.mp4 26.62 MB
  3. F1 Score vs Simple Average.srt 7.02 KB
  4. Precision-Recall Curve.mp4 38.5 MB
  4. Precision-Recall Curve.srt 9.01 KB
  5. ROC and AUC.mp4 86.58 MB
  5. ROC and AUC.srt 18.13 KB
  9. Improving Neural Network Performance - Part (I)
  1. Gradient Descent With Momentum - Part 1.mp4 45.78 MB
  1. Gradient Descent With Momentum - Part 1.srt 11.93 KB
  10. Changing Activation Functions - Tanh - Relu - LeakyRelu.mp4 83.41 MB
  10. Changing Activation Functions - Tanh - Relu - LeakyRelu.srt 18.78 KB
  2. Gradient Descent With Momentum - Part 2.mp4 59.47 MB
  2. Gradient Descent With Momentum - Part 2.srt 15.97 KB
  3. Adagrad And RMSProb.mp4 61.16 MB
  3. Adagrad And RMSProb.srt 14.44 KB
  4. Adam And Learning Rate Decay.mp4 65.6 MB
  4. Adam And Learning Rate Decay.srt 15.14 KB
  5. The Vanishing Gradient Problem.mp4 46.87 MB
  5. The Vanishing Gradient Problem.srt 8.04 KB
  6. Input Centering And Normalization - Part 1.mp4 25.23 MB
  6. Input Centering And Normalization - Part 1.srt 6.69 KB
  7. Input Centering And Normalization - Part 2.mp4 40.33 MB
  7. Input Centering And Normalization - Part 2.srt 10.17 KB
  8. Weight Initialization - Part 1 - The Symmetry Problem.mp4 41.14 MB
  8. Weight Initialization - Part 1 - The Symmetry Problem.srt 9.97 KB
  9. Weight Initialization - Part 2.mp4 42.08 MB
  9. Weight Initialization - Part 2.srt 9.93 KB

Description



Description

Deep Learning is surely one of the hottest topics nowadays, with a tremendous amount of practical applications in many many fields.Those applications include, without being limited to, image classification, object detection, action recognition in videos, motion synthesis, machine translation, self-driving cars, speech recognition, speech and video generation, natural language processing and understanding, robotics, and many many more.

Now you might be wondering :

There is a very large number of courses well-explaining deep learning, why should I prefer this specific course over them ?

The answer is : You shouldn’t ! Most of the other courses heavily focus on “Programming” deep learning applications as fast as possible, without giving detailed explanations on the underlying mathematical foundations that the field of deep learning was built upon. And this is exactly the gap that my course is designed to cover. It is designed to be used hand in hand with other programming courses, not to replace them.

Since this series is heavily mathematical, I will refer many many times during my explanations to sections from my own college level linear algebra course. In general, being quite familiar with linear algebra is a real prerequisite for this course.

Please have a look at the course syllables, and remember : This is only part (I) of the deep learning series!
Who this course is for:

Deep Learning Engineers Or College Students Who Want To Gain Deep Mathematical Understanding Of The Topic

Requirements

You Should Be Familiar With College Level Linear Algebra [Advanced]
You Should Be Familiar With Multi-Variable Calculus And Chain-Rule
You Should Be Famililar With Basic Probability

Last Updated 11/2020

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