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

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College Level Neural Nets [I] - Basic Nets Math & Practice! [TutsNode.com] - College Level Neural Nets [I] - Basic Nets Math & Practice! 6. The Back-Propagation Algorithm !
  • 3. Derivation Of Back Propagation - Part 3.mp4 (127.4 MB)
  • 1. Derivation Of Back Propagation - Part 1.mp4 (51.1 MB)
  • 1. Derivation Of Back Propagation - Part 1.srt (8.0 KB)
  • 2. Derivation Of Back Propagation - Part 2.mp4 (117.3 MB)
  • 2. Derivation Of Back Propagation - Part 2.srt (14.7 KB)
  • 3. Derivation Of Back Propagation - Part 3.srt (16.9 KB)
  • 4. Vectorization Of BackPropagation - Part 1.mp4 (40.1 MB)
  • 4. Vectorization Of BackPropagation - Part 1.srt (6.0 KB)
  • 5. Vectorization Of BackPropagation - Part 2.mp4 (56.5 MB)
  • 5. Vectorization Of BackPropagation - Part 2.srt (10.5 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 (3.4 KB)
  • 8. Vectorization Of BackPropagation - Part 5 - Batch Vectorization.mp4 (76.1 MB)
  • 8. Vectorization Of BackPropagation - Part 5 - Batch Vectorization.srt (15.6 KB)
1. Introduction To Machine Learning
  • 1. Promo Video.mp4 (33.9 MB)
  • 1. Promo Video.srt (2.0 KB)
  • 2. Introduction To Machine Learning.mp4 (36.6 MB)
  • 2. Introduction To Machine Learning.srt (10.9 KB)
  • 2.1 WrittenNotes.rar (13.2 MB)
2. The Linear Perceptron
  • 1. Introduction To The Classification Problem.mp4 (38.3 MB)
  • 1. Introduction To The Classification Problem.srt (8.6 KB)
  • 2. A Simple Glimpse Of Overfitting.mp4 (26.2 MB)
  • 2. A Simple Glimpse Of Overfitting.srt (5.9 KB)
  • 3. The Perceptron Equation.mp4 (42.4 MB)
  • 3. The Perceptron Equation.srt (9.4 KB)
  • 4. Visualization Of The Perceptron Equation.mp4 (27.1 MB)
  • 4. Visualization Of The Perceptron Equation.srt (5.5 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 (9.4 KB)
  • 6. More Visualization For The Perceptron Weights - I.mp4 (66.7 MB)
  • 6. More Visualization For The Perceptron Weights - I.srt (14.5 KB)
  • 7. More Visualization Of The Perceptron Weights - II.mp4 (114.8 MB)
  • 7. More Visualization Of The Perceptron Weights - II.srt (18.3 KB)
  • 8. Activation Functions.mp4 (18.0 MB)
  • 8. Activation Functions.srt (4.8 KB)
  • 9. Graphical Representation Of A Neural Network.mp4 (24.4 MB)
  • 9. Graphical Representation Of A Neural Network.srt (6.2 KB)
  • 10. Types Of Machine Learning.mp4 (54.9 MB)
  • 10. Types Of Machine Learning.srt (11.9 KB)
  • 11. Solved Example (I) Single Layer Perceptron Designed Graphically.mp4 (84.1 MB)
  • 11. Solved Example (I) Single Layer Perceptron Designed Graphically.srt (15.2 KB)
3. Non-Linearly Separable Data And The Multi Layer Perceptron (MLP)
  • 1. Introduction To Multi-Layer Perceptrons.mp4 (97.6 MB)
  • 1. Introduction To Multi-Layer Perceptrons.srt (18.3 KB)
  • 2. Solved Example (II) MLP Design Graphically.mp4 (111.8 MB)
  • 2. Solved Example (II) MLP Design Graphically.srt (16.5 KB)
  • 3. Intuition Of Multi-Layer Perceptrons - Part 1.mp4 (69.8 MB)
  • 3. Intuition Of Multi-Layer Perceptrons - Part 1.srt (13.9 KB)
  • 4. Intuition Of Multi-Layer Perceptrons - Part 2.mp4 (68.0 MB)
  • 4. Intuition Of Multi-Layer Perceptrons - Part 2.srt (11.3 KB)
  • 5. The XOR Problem - Part 1.mp4 (93.2 MB)
  • 5. The XOR Problem - Part 1.srt (19.4 KB)
  • 6. The XOR Problem - Part 2.mp4 (33.3 MB)
  • 6. The XOR Problem - Part 2.srt (6.9 KB)
  • 7. MultiClass Classification And The Sigmoid Activation.mp4 (92.2 MB)
  • 7. MultiClass Classification And The Sigmoid Activation.srt (17.6 KB)
  • 8. Vectorized Notation And The Weight Matrix.mp4 (26.7 MB)
  • 8. Vectorized Notation And The Weight Matrix.srt (3.1 KB)
4. Perceptron Learning !
  • 1. The Perceptron Learning Rule - Part 1.mp4 (22.5 MB)
  • 1. The Perceptron Learning Rule - Part 1.srt (6.7 KB)
  • 2. The Perceptron Learning Rule - Part 2.mp4 (53.4 MB)
  • 2. The Perceptron Learning Rule - Part 2.srt (13.1 KB)
  • 3. Proof Perceptron Convergence Theorem - Part 1.mp4 (102.0 MB)
  • 3. Proof Perceptron Convergence Theorem - Part 1.srt (17.8 KB)
  • 4. Proof Perceptron Convergence Theorem - Part 2.mp4 (22.9 MB)
  • 4. Proof Perceptron Convergence Theorem - Part 2.srt (6.0 KB)
  • 5. Proof Perceptron Convergence Theorem - Part 3.mp4 (48.1 MB)
  • 5. Proof Perceptron Convergence Theorem - Part 3.srt (7.6 KB)
  • 6. Three Main Problems Of The Threshold Perceptron.mp4 (30.2 MB)
  • 6. Three Main Problems Of The Threshold Perceptron.srt (7.7 KB)
5. The Gradient Descent Algorithm
  • 1. The Error Function.mp4 (61.4 MB)
  • 1. The Error Function.srt (11.9 KB)
  • 2. The Sigmoid Activation Function Again.mp4 (53.8 MB)
  • 2. The Sigmoid Activation Function Again.srt (10.4 KB)
  • 3. Deriving The Gradient Descent Algorithm.mp4 (47.8 MB)
  • 3. Deriving The Gradient Descent Algorithm.srt (11.1 KB)
  • 4. Notes About Gradient Descent.mp4 (29.7 MB)
  • 4. Notes About Gradient Descent.srt (6.6 KB)
  • 5. More Notes And filling Up.mp4 (68.3 MB)
  • 5. More Notes And filling Up.srt (12.8 KB)
  • 6. Solved Example (III) Gradient Descent Convergence.mp4 (66.3 MB)
  • 6. Solved Example (III) Gradient Descent Convergence.srt (14.9 KB)
  • 7. Solved Example (IIII) MLP With Linear Activations.mp4 (26.4 MB)
  • 7. Solved Example (IIII) MLP With Linear Activations.srt (4.5 KB)
7. Regularization !
  • 1. Regression, Overfitting, And Underfitting.mp4 (77.0 MB)
  • 1. Regression, Overfitting, And Underfitting.srt (21.9 KB)
  • 2. Introduction To Reglarization.mp4 (69.8 MB)
  • 2. Introduction To Reglarization.srt (12.0 KB)
  • 3. Different Ways For Regularization.mp4 (35.7 MB)
  • 3. Different Ways For Regularization.srt (6.5 KB)
  • 4. L1 vs L2 Regularization - Part 1 - Gradient Descent.mp4 (42.4 MB)
  • 4. L1 vs L2

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

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4.4 GB
seeders:15
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Udemy - College Level Neural Nets [I] – Basic Nets: Math & Practice!

Torrent hash: CB5D4D36E5211DDDF93F102D9C2A452E1D0BB55D