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[ DevCourseWeb com ] Udemy - Python for Deep Learning - Build Neural Networks in Python
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1. Introduction to Deep Learning
1. What is a Deep Learning .mp4
1. What is a Deep Learning .srt
2. Why is Deep Learning Important.mp4
2. Why is Deep Learning Important.srt
3. Software and Frameworks.mp4
3. Software and Frameworks.srt
10. Implementation of CNN in Python
1. Dataset.mp4
1. Dataset.srt
2. Importing libraries.mp4
2. Importing libraries.srt
3. Building the CNN model.mp4
3. Building the CNN model.srt
4. Accuracy of the model.mp4
4. Accuracy of the model.srt
2. Artificial Neural Networks (ANN)
1. Introduction.mp4
1. Introduction.srt
2. Anatomy and function of neurons.mp4
2. Anatomy and function of neurons.srt
3. An introduction to the neural network.mp4
3. An introduction to the neural network.srt
4. Architecture of a neural network.mp4
4. Architecture of a neural network.srt
3. Propagation of information in ANNs
1. Feed-forward and Back Propagation Networks.mp4
1. Feed-forward and Back Propagation Networks.srt
2. Backpropagation In Neural Networks.mp4
2. Backpropagation In Neural Networks.srt
3. Minimizing the cost function using backpropagation.mp4
3. Minimizing the cost function using backpropagation.srt
4. Neural Network Architectures
1. Single layer perceptron (SLP) model.mp4
1. Single layer perceptron (SLP) model.srt
2. Radial Basis Network (RBN).mp4
2. Radial Basis Network (RBN).srt
3. Multi-layer perceptron (MLP) Neural Network.mp4
3. Multi-layer perceptron (MLP) Neural Network.srt
4. Recurrent neural network (RNN).mp4
4. Recurrent neural network (RNN).srt
5. Long Short-Term Memory (LSTM) networks.mp4
5. Long Short-Term Memory (LSTM) networks.srt
6. Hopfield neural network.mp4
6. Hopfield neural network.srt
7. Boltzmann Machine Neural Network.mp4
7. Boltzmann Machine Neural Network.srt
5. Activation Functions
1. What is the Activation Function.mp4
1. What is the Activation Function.srt
2. Important Terminologies.mp4
2. Important Terminologies.srt
3. The sigmoid function.mp4
3. The sigmoid function.srt
4. Hyperbolic tangent function.mp4
4. Hyperbolic tangent function.srt
5. Softmax function.mp4
5. Softmax function.srt
6. Rectified Linear Unit (ReLU) function.mp4
6. Rectified Linear Unit (ReLU) function.srt
7. Leaky Rectified Linear Unit function.mp4
7. Leaky Rectified Linear Unit function.srt
6. Gradient Descent Algorithm
1. What is Gradient Decent.mp4
1. What is Gradient Decent.srt
2. What is Stochastic Gradient Decent.mp4
2. What is Stochastic Gradient Decent.srt
3. Gradient Decent vs Stochastic Gradient Decent.mp4
3. Gradient Decent vs Stochastic Gradient Decent.srt
7. Summary Overview of Neural Networks
1. How artificial neural networks work.mp4
1. How artificial neural networks work.srt
2. Advantages of Neural Networks.mp4
2. Advantages of Neural Networks.srt
3. Disadvantages of Neural Networks.mp4
3. Disadvantages of Neural Networks.srt
4. Applications of Neural Networks.mp4
4. Applications of Neural Networks.srt
8. Implementation of ANN in Python
1. Introduction.mp4
1. Introduction.srt
10. Feature scaling.mp4
10. Feature scaling.srt
11. Building the Artificial Neural Network.mp4
11. Building the Artificial Neural Network.srt
12. Adding the input layer and the first hidden layer.mp4
12. Adding the input layer and the first hidden layer.srt
13. Adding the next hidden layer.mp4
13. Adding the next hidden layer.srt
14. Adding the output layer.mp4
14. Adding the output layer.srt
15. Compiling the artificial neural network.mp4
15. Compiling the artificial neural network.srt
16. Fitting the ANN model to the training set.mp4
16. Fitting the ANN model to the training set.srt
17. Predicting the test set results.mp4
17. Predicting the test set results.srt
2. Exploring the dataset.mp4
2. Exploring the dataset.srt
3. Problem Statement.mp4
3. Problem Statement.srt
4. Data Pre-processing.mp4
4. Data Pre-processing.srt
5. Loading the dataset.mp4
5. Loading the dataset.srt
6. Splitting the dataset into independent and dependent variables.mp4
6. Splitting the dataset into independent and dependent variables.srt
7. Label encoding using scikit-learn.mp4
7. Label encoding using scikit-learn.srt
8. One-hot encoding using scikit-learn.mp4
8. One-hot encoding using scikit-learn.srt
9. Training and Test Sets Splitting Data.mp4
9. Training and Test Sets Splitting Data.srt
9. Convolutional Neural Networks (CNN)
1. Introduction.mp4
1. Introduction.srt
2. Components of convolutional neural networks.mp4
2. Components of convolutional neural networks.srt
3. Convolution Layer.mp4
3. Convolution Layer.srt
4. Pooling Layer.mp4
4. Pooling Layer.srt
5. Fully connected Layer.mp4
5. Fully connected Layer.srt
Bonus Resources.txt
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