Python for Deep Learning Build Neural NetWorks in Python

Python for Deep Learning Build Neural Networks in Python
Language: English
Files Type: pdf, csv, mkv, html, ipynb| Size: 939.57 MB
Video: 02:04:01 | 1920X1080 | 729 Kbps
Audio: A_AAC-2 | 62 Kbps | AAC
Genre:eLearning



Videos Files :
001 What is a Deep Learning.mkv (13.29 MB)
003 Why is Deep Learning Important.mkv (8.33 MB)
004 Software and Frameworks.mkv (6.18 MB)
001 Introduction.mkv (11.47 MB)
002 Anatomy and function of neurons.mkv (8.36 MB)
003 An introduction to the neural network.mkv (13.36 MB)
004 Architecture of a neural network.mkv (11.97 MB)
001 Feed forward and Back Propagation Networks.mkv (6.89 MB)
002 Backpropagation In Neural Networks.mkv (6.75 MB)
003 Minimizing the cost function using backpropagation.mkv (5.22 MB)
001 Single layer perceptron (SLP) model.mkv (5.23 MB)
002 Radial Basis Network (RBN).mkv (4.98 MB)
003 Multi layer perceptron (MLP) Neural Network.mkv (5.39 MB)
004 Recurrent neural network (RNN).mkv (7.04 MB)
005 Long Short Term Memory (LSTM) networks.mkv (7.7 MB)
006 Hopfield neural network.mkv (6.18 MB)
007 Boltzmann Machine Neural Network.mkv (5.34 MB)
001 What is the Activation Function.mkv (10.33 MB)
002 Important Terminologies.mkv (4.86 MB)
003 The sigmoid function.mkv (8.03 MB)
004 Hyperbolic tangent function.mkv (7.56 MB)
005 Softmax function.mkv (4.41 MB)
006 Rectified Linear Unit (ReLU) function.mkv (5.6 MB)
007 Leaky Rectified Linear Unit function.mkv (4.13 MB)
001 What is Gradient Decent.mkv (10.59 MB)
002 What is Stochastic Gradient Decent.mkv (6.22 MB)
003 Gradient Decent vs Stochastic Gradient Decent.mkv (7.53 MB)
001 How artificial neural networks work.mkv (28.45 MB)
002 Advantages of Neural Networks.mkv (4.14 MB)
003 Disadvantages of Neural Networks.mkv (3.35 MB)
004 Applications of Neural Networks.mkv (6.88 MB)
001 Introduction.mkv (4.95 MB)
002 Exploring the dataset.mkv (13.47 MB)
003 Problem Statement.mkv (3.13 MB)
004 Data Pre processing.mkv (11.64 MB)
005 Loading the dataset.mkv (12.09 MB)
006 Splitting the dataset into independent and dependent variables.mkv (29.41 MB)
007 Label encoding using scikit learn.mkv (34.3 MB)
008 One hot encoding using scikit learn.mkv (49.61 MB)
009 Training and Test Sets Splitting Data.mkv (33.18 MB)
010 Feature scaling.mkv (28.6 MB)
011 Building the Artificial Neural Network.mkv (20.87 MB)
012 Adding the input layer and the first hidden layer.mkv (32.27 MB)
013 Adding the next hidden layer.mkv (14.78 MB)
014 Adding the output layer.mkv (16.41 MB)
015 Compiling the artificial neural network.mkv (33.87 MB)
016 Fitting the ANN model to the training set.mkv (40.24 MB)
017 Predicting the test set results.mkv (14.94 MB)
001 Introduction.mkv (27.45 MB)
002 Components of convolutional neural networks.mkv (6.64 MB)
003 Convolution Layer.mkv (13.27 MB)
004 Pooling Layer.mkv (10.7 MB)
005 Fully connected Layer.mkv (12.55 MB)
001 Dataset.mkv (7.39 MB)
002 Importing libraries.mkv (13.41 MB)
003 Building the CNN model.mkv (69.03 MB)
004 Accuracy of the model.mkv (9.88 MB)