[DesireCourse.Com] Udemy - Artificial Intelligence II - Neural Networks in Java

Creation Time	March 24, 2025, 7:19 p.m.
Last Access Time	Oct. 21, 2025, 3:57 p.m.
File Size	818.3 MB
Keywords	Java Neural DesireCourse in - Artificial Udemy II Intelligence Com Networks
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Total Files	125
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2. Neural Networks Introduction/2. Modeling human brain.mp4 93.9 MB
11. Optical Character Recognition (OCR)/5. OCR with neural network.mp4 43.9 MB
10. Classification - Iris Dataset/3. Testing the neural network.mp4 34.2 MB
4. Neural Networks With Backpropagation Theory/2. Optimization - cost function.mp4 25.9 MB
4. Neural Networks With Backpropagation Theory/8. Gradient calculation I - output layer.mp4 20.3 MB
4. Neural Networks With Backpropagation Theory/4. Simplified feedforward network.mp4 19.4 MB
2. Neural Networks Introduction/1. Axons and neurons in the human brain.mp4 19.3 MB
3. Hopfield Neural Network/7. Hopfield network implementation II - matrix operations.mp4 19.1 MB
3. Hopfield Neural Network/8. Hopfield network implementation III - network.mp4 18.6 MB
4. Neural Networks With Backpropagation Theory/13. Resilient propagation.mp4 18.6 MB
4. Neural Networks With Backpropagation Theory/1. Feedforward neural networks.mp4 18.4 MB
7. Backpropagation Implementation/3. Backpropagation implementation II - NeuralNetwork.mp4 18.3 MB
8. Logical Operators/2. Running the neural network AND.mp4 18.0 MB
2. Neural Networks Introduction/4. Artificial neurons - the model.mp4 16.5 MB
7. Backpropagation Implementation/5. Backpropagation implementation IV - run.mp4 16.4 MB
7. Backpropagation Implementation/6. Backpropagation implementation V - train.mp4 16.0 MB
4. Neural Networks With Backpropagation Theory/5. Feedforward neural network topology.mp4 14.7 MB
2. Neural Networks Introduction/5. Artificial neurons - activations functions.mp4 14.2 MB
4. Neural Networks With Backpropagation Theory/7. Error calculation.mp4 13.7 MB
6. Single Perceptron Model/4. Perceptron model implementation III.mp4 13.7 MB
9. Clustering/2. Clustering with neural networks II.mp4 13.5 MB
4. Neural Networks With Backpropagation Theory/6. The learning algorithm.mp4 13.3 MB
3. Hopfield Neural Network/5. Hopfield neural network example.mp4 13.2 MB
7. Backpropagation Implementation/1. Structure of the feedforward network.mp4 13.1 MB
6. Single Perceptron Model/3. Perceptron model implementation II.mp4 12.8 MB
4. Neural Networks With Backpropagation Theory/10. Backpropagation.mp4 12.7 MB
7. Backpropagation Implementation/4. Backpropagation implementation III - Layer.mp4 12.1 MB
3. Hopfield Neural Network/1. Hopfield neural network introduction.mp4 11.8 MB
3. Hopfield Neural Network/3. Hopfield neural network training and learning.mp4 11.5 MB
6. Single Perceptron Model/2. Perceptron model implementation I.mp4 11.5 MB
2. Neural Networks Introduction/7. Artificial neurons - an example.mp4 11.4 MB
2. Neural Networks Introduction/8. Neural networks - the big picture.mp4 10.8 MB
7. Backpropagation Implementation/2. Backpropagation implementation I - activation function.mp4 9.8 MB
11. Optical Character Recognition (OCR)/3. Transform an image into numerical data.mp4 9.8 MB
4. Neural Networks With Backpropagation Theory/15. Applications of neural networks II - stock market forecast.mp4 9.5 MB
4. Neural Networks With Backpropagation Theory/16. Deep learning.mp4 9.5 MB
3. Hopfield Neural Network/6. Hopfield network implementation I - utils.mp4 9.4 MB
3. Hopfield Neural Network/2. Hopfield network energy.mp4 9.4 MB
4. Neural Networks With Backpropagation Theory/9. Gradient calculation II - hidden layer.mp4 9.2 MB
4. Neural Networks With Backpropagation Theory/14. Applications of neural networks I - character recognition.mp4 8.8 MB
3. Hopfield Neural Network/9. Hopfield network implementation IV - running the application.mp4 8.8 MB
8. Logical Operators/3. Running the neural network OR.mp4 8.1 MB
10. Classification - Iris Dataset/2. Constructing the neural network.mp4 8.0 MB
11. Optical Character Recognition (OCR)/1. Optical character recognition theory.mp4 7.9 MB
11. Optical Character Recognition (OCR)/4. Creating the datasets.mp4 7.6 MB
3. Hopfield Neural Network/4. Hopfield neural network problems.mp4 7.2 MB
10. Classification - Iris Dataset/1. About the Iris dataset.mp4 7.1 MB
1. Introduction/1. Introduction.mp4 6.9 MB
2. Neural Networks Introduction/3. Learning paradigms.mp4 6.9 MB
6. Single Perceptron Model/6. Conclusion linearity and hidden layers.mp4 6.7 MB
8. Logical Operators/4. Running the neural network XOR.mp4 5.6 MB
5. Types of Neural Networks/1. Types of neural networks.mp4 5.5 MB
2. Neural Networks Introduction/9. Applications of neural networks.mp4 5.2 MB
11. Optical Character Recognition (OCR)/2. Installing paint.net.mp4 5.2 MB
9. Clustering/1. Clustering with neural networks I.mp4 4.8 MB
6. Single Perceptron Model/1. Perceptron model training.mp4 4.7 MB
4. Neural Networks With Backpropagation Theory/11. Backpropagation II.mp4 4.7 MB
8. Logical Operators/1. Logical operators introduction.mp4 4.6 MB
6. Single Perceptron Model/5. Trying to solve XOR problem.mp4 4.5 MB
12. Course Materials (DOWNLOADS)/1.1 neural_networks.zip.zip 2.0 MB
4. Neural Networks With Backpropagation Theory/2. Optimization - cost function.vtt 11.9 KB
4. Neural Networks With Backpropagation Theory/8. Gradient calculation I - output layer.vtt 9.3 KB
2. Neural Networks Introduction/1. Axons and neurons in the human brain.vtt 9.2 KB
4. Neural Networks With Backpropagation Theory/4. Simplified feedforward network.vtt 9.0 KB
4. Neural Networks With Backpropagation Theory/1. Feedforward neural networks.vtt 8.9 KB
2. Neural Networks Introduction/2. Modeling human brain.vtt 8.3 KB
7. Backpropagation Implementation/3. Backpropagation implementation II - NeuralNetwork.vtt 7.8 KB
8. Logical Operators/2. Running the neural network AND.vtt 7.7 KB
2. Neural Networks Introduction/4. Artificial neurons - the model.vtt 7.4 KB
3. Hopfield Neural Network/7. Hopfield network implementation II - matrix operations.vtt 7.3 KB
3. Hopfield Neural Network/8. Hopfield network implementation III - network.vtt 7.2 KB
7. Backpropagation Implementation/5. Backpropagation implementation IV - run.vtt 7.1 KB
10. Classification - Iris Dataset/3. Testing the neural network.vtt 6.8 KB
7. Backpropagation Implementation/6. Backpropagation implementation V - train.vtt 6.7 KB
11. Optical Character Recognition (OCR)/5. OCR with neural network.vtt 6.7 KB
7. Backpropagation Implementation/1. Structure of the feedforward network.vtt 6.7 KB
4. Neural Networks With Backpropagation Theory/5. Feedforward neural network topology.vtt 6.6 KB
2. Neural Networks Introduction/5. Artificial neurons - activations functions.vtt 6.6 KB
4. Neural Networks With Backpropagation Theory/7. Error calculation.vtt 6.5 KB
3. Hopfield Neural Network/5. Hopfield neural network example.vtt 6.3 KB
4. Neural Networks With Backpropagation Theory/6. The learning algorithm.vtt 6.0 KB
4. Neural Networks With Backpropagation Theory/10. Backpropagation.vtt 5.7 KB
6. Single Perceptron Model/4. Perceptron model implementation III.vtt 5.7 KB
6. Single Perceptron Model/3. Perceptron model implementation II.vtt 5.6 KB
3. Hopfield Neural Network/3. Hopfield neural network training and learning.vtt 5.6 KB
3. Hopfield Neural Network/1. Hopfield neural network introduction.vtt 5.5 KB
9. Clustering/2. Clustering with neural networks II.vtt 5.2 KB
4. Neural Networks With Backpropagation Theory/13. Resilient propagation.vtt 5.1 KB
13. DISCOUNT FOR OTHER COURSES!/1. 90% OFF For Other Courses.html 5.1 KB
7. Backpropagation Implementation/4. Backpropagation implementation III - Layer.vtt 4.9 KB
2. Neural Networks Introduction/8. Neural networks - the big picture.vtt 4.8 KB
6. Single Perceptron Model/2. Perceptron model implementation I.vtt 4.8 KB
2. Neural Networks Introduction/7. Artificial neurons - an example.vtt 4.8 KB
11. Optical Character Recognition (OCR)/3. Transform an image into numerical data.vtt 4.8 KB
4. Neural Networks With Backpropagation Theory/15. Applications of neural networks II - stock market forecast.vtt 4.7 KB
4. Neural Networks With Backpropagation Theory/16. Deep learning.vtt 4.6 KB
7. Backpropagation Implementation/2. Backpropagation implementation I - activation function.vtt 4.5 KB
3. Hopfield Neural Network/2. Hopfield network energy.vtt 4.5 KB
4. Neural Networks With Backpropagation Theory/14. Applications of neural networks I - character recognition.vtt 4.4 KB
5. Types of Neural Networks/1. Types of neural networks.vtt 4.4 KB
3. Hopfield Neural Network/9. Hopfield network implementation IV - running the application.vtt 4.3 KB
4. Neural Networks With Backpropagation Theory/9. Gradient calculation II - hidden layer.vtt 4.1 KB
3. Hopfield Neural Network/6. Hopfield network implementation I - utils.vtt 4.0 KB
11. Optical Character Recognition (OCR)/1. Optical character recognition theory.vtt 3.9 KB
8. Logical Operators/3. Running the neural network OR.vtt 3.6 KB
1. Introduction/1. Introduction.vtt 3.6 KB
3. Hopfield Neural Network/4. Hopfield neural network problems.vtt 3.6 KB
6. Single Perceptron Model/6. Conclusion linearity and hidden layers.vtt 3.3 KB
2. Neural Networks Introduction/3. Learning paradigms.vtt 3.0 KB
10. Classification - Iris Dataset/1. About the Iris dataset.vtt 3.0 KB
10. Classification - Iris Dataset/2. Constructing the neural network.vtt 2.9 KB
11. Optical Character Recognition (OCR)/2. Installing paint.net.vtt 2.7 KB
8. Logical Operators/4. Running the neural network XOR.vtt 2.6 KB
9. Clustering/1. Clustering with neural networks I.vtt 2.4 KB
2. Neural Networks Introduction/9. Applications of neural networks.vtt 2.4 KB
11. Optical Character Recognition (OCR)/4. Creating the datasets.vtt 2.4 KB
6. Single Perceptron Model/1. Perceptron model training.vtt 2.3 KB
8. Logical Operators/1. Logical operators introduction.vtt 2.0 KB
4. Neural Networks With Backpropagation Theory/11. Backpropagation II.vtt 2.0 KB
6. Single Perceptron Model/5. Trying to solve XOR problem.vtt 1.7 KB
4. Neural Networks With Backpropagation Theory/3. ARTICLE optimization algorithms.html 230 bytes
4. Neural Networks With Backpropagation Theory/12. ARTICLE derivation of backpropagation.html 171 bytes
2. Neural Networks Introduction/6. ARTICLE activation functions.html 169 bytes
12. Course Materials (DOWNLOADS)/1. Course materials.html 60 bytes
[DesireCourse.Com].url 51 bytes

[DesireCourse.Com] Udemy - Artificial Intelligence II - Neural Networks in Java

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