Udemy - Python for Deep Learning - Build Neural Networks in Pytho...

Files:

• Get Bonus Downloads Here.url (0.2 KB)
~Get Your Files Here ! 1 - Introduction to Deep Learning
• 1 - What is a Deep Learning.en_US.vtt (3.4 KB)
• 1 - What is a Deep Learning.mp4 (11.6 MB)
• 2 - Course Materials - ANN_Codes.ipynb (2.7 MB)
• 2 - Course Materials - CNN_Codes.ipynb (5.2 KB)
• 2 - Course Materials - Churn_Modelling.csv (668.8 KB)
• 2 - Course Materials - Course Slides.pdf (4.3 MB)
• 2 - Course Materials - mnist_test.csv (17.5 MB)
• 2 - Course Materials - mnist_train.csv (104.6 MB)
• 2 - Course Materials.html (0.1 KB)
• 3 - Why is Deep Learning Important.en_US.vtt (1.8 KB)
• 3 - Why is Deep Learning Important.mp4 (7.1 MB)
• 4 - Software and Frameworks.en_US.vtt (0.8 KB)
• 4 - Software and Frameworks.mp4 (5.4 MB)
10 - Implementation of CNN in Python
• 1 - Dataset.en_US.vtt (0.8 KB)
• 1 - Dataset.mp4 (6.2 MB)
• 2 - Importing libraries.en_US.vtt (2.1 KB)
• 2 - Importing libraries.mp4 (11.1 MB)
• 3 - Building the CNN model.en_US.vtt (9.7 KB)
• 3 - Building the CNN model.mp4 (47.6 MB)
• 4 - Accuracy of the model.en_US.vtt (0.7 KB)
• 4 - Accuracy of the model.mp4 (8.8 MB)
11 - BONUS Section - Don't Miss Out
• 1 - BONUS Section - Don't Miss Out.html (0.9 KB)
2 - Artificial Neural Networks (ANN)
• 1 - Introduction.en_US.vtt (1.3 KB)
• 1 - Introduction.mp4 (8.9 MB)
• 2 - Anatomy and function of neurons.en_US.vtt (1.3 KB)
• 2 - Anatomy and function of neurons.mp4 (7.2 MB)
• 3 - An introduction to the neural network.en_US.vtt (3.1 KB)
• 3 - An introduction to the neural network.mp4 (11.5 MB)
• 4 - Architecture of a neural network.en_US.vtt (1.5 KB)
• 4 - Architecture of a neural network.mp4 (9.1 MB)
3 - Propagation of information in ANNs
• 1 - Feed-forward and Back Propagation Networks.en_US.vtt (1.1 KB)
• 1 - Feed-forward and Back Propagation Networks.mp4 (5.8 MB)
• 2 - Backpropagation In Neural Networks.en_US.vtt (0.8 KB)
• 2 - Backpropagation In Neural Networks.mp4 (5.4 MB)
• 3 - Minimizing the cost function using backpropagation.en_US.vtt (1.4 KB)
• 3 - Minimizing the cost function using backpropagation.mp4 (5.0 MB)
4 - Neural Network Architectures
• 1 - Single layer perceptron (SLP) model.en_US.vtt (1.0 KB)
• 1 - Single layer perceptron (SLP) model.mp4 (4.7 MB)
• 2 - Radial Basis Network (RBN).en_US.vtt (0.8 KB)
• 2 - Radial Basis Network (RBN).mp4 (4.4 MB)
• 3 - Multi-layer perceptron (MLP) Neural Network.en_US.vtt (0.7 KB)
• 3 - Multi-layer perceptron (MLP) Neural Network.mp4 (4.7 MB)
• 4 - Recurrent neural network (RNN).en_US.vtt (1.1 KB)
• 4 - Recurrent neural network (RNN).mp4 (6.0 MB)
• 5 - Long Short-Term Memory (LSTM) networks.en_US.vtt (1.3 KB)
• 5 - Long Short-Term Memory (LSTM) networks.mp4 (6.5 MB)
• 6 - Hopfield neural network.en_US.vtt (1.1 KB)
• 6 - Hopfield neural network.mp4 (5.3 MB)
• 7 - Boltzmann Machine Neural Network.en_US.vtt (0.8 KB)
• 7 - Boltzmann Machine Neural Network.mp4 (4.7 MB)
5 - Activation Functions
• 1 - What is the Activation Function.en_US.vtt (1.6 KB)
• 1 - What is the Activation Function.mp4 (8.6 MB)
• 2 - Important Terminologies.en_US.vtt (0.7 KB)
• 2 - Important Terminologies.mp4 (4.6 MB)
• 3 - The sigmoid function.en_US.vtt (2.0 KB)
• 3 - The sigmoid function.mp4 (7.1 MB)
• 4 - Hyperbolic tangent function.en_US.vtt (1.2 KB)
• 4 - Hyperbolic tangent function.mp4 (6.3 MB)
• 5 - Softmax function.en_US.vtt (0.8 KB)
• 5 - Softmax function.mp4 (4.2 MB)
• 6 - Rectified Linear Unit (ReLU) function.en_US.vtt (1.4 KB)
• 6 - Rectified Linear Unit (ReLU) function.mp4 (5.3 MB)
• 7 - Leaky Rectified Linear Unit function.en_US.vtt (0.8 KB)
• 7 - Leaky Rectified Linear Unit function.mp4 (4.0 MB)
6 - Gradient Descent Algorithm
• 1 - What is Gradient Decent.en_US.vtt (1.8 KB)
• 1 - What is Gradient Decent.mp4 (9.4 MB)
• 2 - What is Stochastic Gradient Decent.en_US.vtt (1.8 KB)
• 2 - What is Stochastic Gradient Decent.mp4 (6.0 MB)
• 3 - Gradient Decent vs Stochastic Gradient Decent.en_US.vtt (0.7 KB)
• 3 - Gradient Decent vs Stochastic Gradient Decent.mp4 (6.2 MB)
7 - Summary Overview of Neural Networks
• 1 - How artificial neural networks work.en_US.vtt (3.4 KB)
• 1 - How artificial neural networks work.mp4 (23.2 MB)
• 2 - Advantages of Neural Networks.en_US.vtt (1.1 KB)
• 2 - Advantages of Neural Networks.mp4 (4.2 MB)
• 3 - Disadvantages of Neural Networks.en_US.vtt (0.7 KB)
• 3 - Disadvantages of Neural Networks.mp4 (3.4 MB)
• 4 - Applications of Neural Networks.en_US.vtt (1.8 KB)
• 4 - Applications of Neural Networks.mp4 (6.4 MB)
8 - Implementation of ANN in Python
• 1 - Introduction.en_US.vtt (0.6 KB)
• 1 - Introduction.mp4 (4.7 MB)
• 10 - Feature scaling.en_US.vtt (3.4 KB)
• 10 - Feature scaling.mp4 (23.4 MB)
• 11 - Building the Artificial Neural Network.en_US.vtt (1.7 KB)
• 11 - Building the Artificial Neural Network.mp4 (15.9 MB)
• 12 - Adding the input layer and the first hidden layer.en_US.vtt (2.8 KB)
• 12 - Adding the input layer and the first hidden layer.mp4 (23.5 MB)
• 13 - Adding the next hidden layer.en_US.vtt (1.1 KB)
• 13 - Adding the next hidden layer.mp4 (11.2 MB)
• 14 - Adding the output layer.en_US.vtt (1.4 KB)
• 14 - Adding the output layer.mp4 (12.2 MB)
• 15 - Compiling the artificial neural network.en_US.vtt (2.6 KB)
• 15 - Compiling the artificial neural network.mp4 (19.6 MB)
• 16 - Fitting the ANN model to the training set.en_US.vtt (2.0 KB)
• 16 - Fitting the ANN model to the training set.mp4 (22.4 MB)
• 17 - Predicting the test set results.en_US.vtt (4.1 KB)
• 17 - Predicting the test set results.mp4 (25.9 MB)
• 2 - Exploring the dataset.en_US.vtt (1.1 KB)
• 2 - Exploring the dataset.mp4 (11.5 MB)
• 3 - Problem Statement.en_US.vtt (0.7 KB)
• 3 - Problem Statement.mp4 (3.2 MB)
• 4 - Data Pre-processing.en_US.vtt (3.5 KB)
• 4 - Data Pre-processing.mp4 (13.7 MB)
• 5 - Loading the dataset.en_US.vtt (1.1 KB)
• 5 - Loading the dataset.mp4 (9.2 MB)
• 6 - Splitting the dataset into independent and dependent variables.en_US.vtt (2.8 KB)
• 6 - Splitting the dataset into independent and dependent variables.mp4 (22.8 MB)
• 7 - Label encoding using scikit-learn.en_US.vtt (3.9 KB)
• 7 - Label encoding using scikit-learn.mp4 (28.0 MB)
• 8 - One-hot encoding using scikit-learn.en_US.vtt (5.8 KB)
• 8 - One-hot encoding using scikit-learn.mp4 (37.9 MB)
• 9 - Training and Test Sets Splitting Data.en_US.vtt (3.1 KB)
• 9 - Training and Test Sets Splitting Data.mp4 (26.4 MB)
9 - Convolutional Neural Networks (CNN)
• 1 - Introduction.en_US.vtt (3.8 KB)
• 1 - Introduction.mp4 (21.0 MB)
• 2 - Components of convolutional neural networks.en_US.vtt (0.9 KB)
• 2 - Components of convolutional neural networks.mp4 (5.9 MB)
• 3 - Convolution Layer.en_US.vtt (3.2 KB)
• 3 - Convolution Layer.mp4 (12.0 MB)
• 4 - Pooling Layer.en_US.vtt (1.8 KB)
• 4 - Pooling Layer.mp4 (9.7 MB)
• 5 - Fully connected Layer.en_US.vtt (1.7 KB)
• 5 - Fully connected Layer.mp4 (9.4 MB)
• Bonus Resources.txt (0.1 KB)