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1 - BONUS Section - Don't Miss Out.html
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HTML
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921.6 B
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1 - Dataset.en_US.vtt
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VTT
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819.2 B
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1 - Dataset.mp4
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6.2 MB
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1 - Feed-forward and Back Propagation Networks.en_US.vtt
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1.1 KB
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1 - Feed-forward and Back Propagation Networks.mp4
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5.8 MB
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1 - How artificial neural networks work.en_US.vtt
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3.4 KB
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1 - How artificial neural networks work.mp4
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23.2 MB
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1 - Introduction.en_US.vtt
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3.8 KB
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1 - Introduction.mp4
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21 MB
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1 - Single layer perceptron (SLP) model.en_US.vtt
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1 KB
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1 - Single layer perceptron (SLP) model.mp4
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4.7 MB
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1 - What is Gradient Decent.en_US.vtt
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1.8 KB
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1 - What is Gradient Decent.mp4
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9.4 MB
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1 - What is a Deep Learning.en_US.vtt
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3.4 KB
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1 - What is a Deep Learning.mp4
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11.6 MB
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1 - What is the Activation Function.en_US.vtt
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1.6 KB
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1 - What is the Activation Function.mp4
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8.6 MB
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10 - Feature scaling.en_US.vtt
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3.4 KB
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10 - Feature scaling.mp4
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23.4 MB
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11 - Building the Artificial Neural Network.en_US.vtt
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1.7 KB
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11 - Building the Artificial Neural Network.mp4
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15.9 MB
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12 - Adding the input layer and the first hidden layer.en_US.vtt
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2.8 KB
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12 - Adding the input layer and the first hidden layer.mp4
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23.5 MB
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13 - Adding the next hidden layer.en_US.vtt
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1.1 KB
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13 - Adding the next hidden layer.mp4
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11.2 MB
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14 - Adding the output layer.en_US.vtt
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1.4 KB
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14 - Adding the output layer.mp4
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12.2 MB
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15 - Compiling the artificial neural network.en_US.vtt
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2.6 KB
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15 - Compiling the artificial neural network.mp4
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19.6 MB
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16 - Fitting the ANN model to the training set.en_US.vtt
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2 KB
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16 - Fitting the ANN model to the training set.mp4
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22.4 MB
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17 - Predicting the test set results.en_US.vtt
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4.1 KB
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17 - Predicting the test set results.mp4
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25.9 MB
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2 - Advantages of Neural Networks.en_US.vtt
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1.1 KB
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2 - Advantages of Neural Networks.mp4
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4.2 MB
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2 - Anatomy and function of neurons.en_US.vtt
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1.3 KB
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2 - Anatomy and function of neurons.mp4
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7.2 MB
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2 - Backpropagation In Neural Networks.en_US.vtt
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819.2 B
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2 - Backpropagation In Neural Networks.mp4
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5.4 MB
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2 - Components of convolutional neural networks.en_US.vtt
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921.6 B
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2 - Components of convolutional neural networks.mp4
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5.9 MB
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2 - Course Materials - ANN_Codes.ipynb
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IPYNB
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2.7 MB
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2 - Course Materials - CNN_Codes.ipynb
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IPYNB
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5.2 KB
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2 - Course Materials - Churn_Modelling.csv
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CSV
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668.8 KB
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2 - Course Materials - Course Slides.pdf
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PDF
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4.3 MB
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2 - Course Materials - mnist_test.csv
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CSV
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17.5 MB
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2 - Course Materials - mnist_train.csv
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CSV
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104.6 MB
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2 - Course Materials.html
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HTML
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102.4 B
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2 - Exploring the dataset.en_US.vtt
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1.1 KB
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2 - Exploring the dataset.mp4
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11.5 MB
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2 - Important Terminologies.en_US.vtt
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716.8 B
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2 - Important Terminologies.mp4
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4.6 MB
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2 - Importing libraries.en_US.vtt
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2.1 KB
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2 - Importing libraries.mp4
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11.1 MB
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2 - Radial Basis Network (RBN).en_US.vtt
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819.2 B
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2 - Radial Basis Network (RBN).mp4
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4.4 MB
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2 - What is Stochastic Gradient Decent.en_US.vtt
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1.8 KB
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2 - What is Stochastic Gradient Decent.mp4
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6 MB
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3 - An introduction to the neural network.en_US.vtt
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3.1 KB
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3 - An introduction to the neural network.mp4
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11.5 MB
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3 - Building the CNN model.en_US.vtt
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9.7 KB
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3 - Building the CNN model.mp4
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47.6 MB
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3 - Convolution Layer.en_US.vtt
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3.2 KB
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3 - Convolution Layer.mp4
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12 MB
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3 - Disadvantages of Neural Networks.en_US.vtt
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716.8 B
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3 - Disadvantages of Neural Networks.mp4
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3.4 MB
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3 - Gradient Decent vs Stochastic Gradient Decent.en_US.vtt
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716.8 B
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3 - Gradient Decent vs Stochastic Gradient Decent.mp4
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6.2 MB
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3 - Minimizing the cost function using backpropagation.en_US.vtt
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1.4 KB
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3 - Minimizing the cost function using backpropagation.mp4
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5 MB
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3 - Multi-layer perceptron (MLP) Neural Network.en_US.vtt
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VTT
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716.8 B
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3 - Multi-layer perceptron (MLP) Neural Network.mp4
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4.7 MB
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3 - Problem Statement.en_US.vtt
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716.8 B
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3 - Problem Statement.mp4
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3.2 MB
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3 - The sigmoid function.en_US.vtt
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2 KB
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3 - The sigmoid function.mp4
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7.1 MB
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3 - Why is Deep Learning Important.en_US.vtt
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1.8 KB
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3 - Why is Deep Learning Important.mp4
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7.1 MB
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4 - Accuracy of the model.en_US.vtt
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716.8 B
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4 - Accuracy of the model.mp4
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8.8 MB
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4 - Applications of Neural Networks.en_US.vtt
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1.8 KB
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4 - Applications of Neural Networks.mp4
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6.4 MB
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4 - Architecture of a neural network.en_US.vtt
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1.5 KB
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4 - Architecture of a neural network.mp4
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9.1 MB
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4 - Data Pre-processing.en_US.vtt
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3.5 KB
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4 - Data Pre-processing.mp4
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13.7 MB
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4 - Hyperbolic tangent function.en_US.vtt
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1.2 KB
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4 - Hyperbolic tangent function.mp4
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6.3 MB
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4 - Pooling Layer.en_US.vtt
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1.8 KB
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4 - Pooling Layer.mp4
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9.7 MB
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4 - Recurrent neural network (RNN).en_US.vtt
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1.1 KB
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4 - Recurrent neural network (RNN).mp4
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6 MB
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4 - Software and Frameworks.en_US.vtt
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819.2 B
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4 - Software and Frameworks.mp4
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5.4 MB
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5 - Fully connected Layer.en_US.vtt
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1.7 KB
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5 - Fully connected Layer.mp4
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9.4 MB
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5 - Loading the dataset.en_US.vtt
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1.1 KB
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5 - Loading the dataset.mp4
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MP4
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9.2 MB
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5 - Long Short-Term Memory (LSTM) networks.en_US.vtt
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1.3 KB
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5 - Long Short-Term Memory (LSTM) networks.mp4
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6.5 MB
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5 - Softmax function.en_US.vtt
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819.2 B
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5 - Softmax function.mp4
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4.2 MB
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6 - Hopfield neural network.en_US.vtt
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1.1 KB
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6 - Hopfield neural network.mp4
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5.3 MB
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6 - Rectified Linear Unit (ReLU) function.en_US.vtt
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1.4 KB
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6 - Rectified Linear Unit (ReLU) function.mp4
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5.3 MB
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6 - Splitting the dataset into independent and dependent variables.en_US.vtt
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2.8 KB
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6 - Splitting the dataset into independent and dependent variables.mp4
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22.8 MB
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7 - Boltzmann Machine Neural Network.en_US.vtt
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819.2 B
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7 - Boltzmann Machine Neural Network.mp4
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MP4
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4.7 MB
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7 - Label encoding using scikit-learn.en_US.vtt
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3.9 KB
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7 - Label encoding using scikit-learn.mp4
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28 MB
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7 - Leaky Rectified Linear Unit function.en_US.vtt
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VTT
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819.2 B
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7 - Leaky Rectified Linear Unit function.mp4
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MP4
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4 MB
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8 - One-hot encoding using scikit-learn.en_US.vtt
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5.8 KB
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8 - One-hot encoding using scikit-learn.mp4
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MP4
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37.9 MB
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9 - Training and Test Sets Splitting Data.en_US.vtt
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3.1 KB
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9 - Training and Test Sets Splitting Data.mp4
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26.4 MB
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Bonus Resources.txt
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TXT
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102.4 B
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Get Bonus Downloads Here.url
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URL
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204.8 B
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