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  1 - Introduction to Deep Learning

   1 - What is a Deep Learning.mp4

   2 - Course Materials - CNN_Codes.ipynb

   2 - Course Materials - Course Slides.pdf

   2 - Course Materials - mnist_train.csv

   3 - Why is Deep Learning Important.en_US.vtt

   4 - Software and Frameworks.en_US.vtt

  10 - Implementation of CNN in Python

   1 - Dataset.mp4

   2 - Importing libraries.mp4

   3 - Building the CNN model.mp4

   4 - Accuracy of the model.mp4

  11 - BONUS Section - Don't Miss Out

  2 - Artificial Neural Networks (ANN)

   1 - Introduction.mp4

   2 - Anatomy and function of neurons.mp4

   3 - An introduction to the neural network.mp4

   4 - Architecture of a neural network.mp4

  3 - Propagation of information in ANNs

   1 - Feed-forward and Back Propagation Networks.mp4

   2 - Backpropagation In Neural Networks.mp4

   3 - Minimizing the cost function using backpropagation.mp4

  4 - Neural Network Architectures

   1 - Single layer perceptron (SLP) model.mp4

   2 - Radial Basis Network (RBN).mp4

   3 - Multi-layer perceptron (MLP) Neural Network.mp4

   4 - Recurrent neural network (RNN).mp4

   5 - Long Short-Term Memory (LSTM) networks.mp4

   6 - Hopfield neural network.mp4

   7 - Boltzmann Machine Neural Network.mp4

  5 - Activation Functions

   1 - What is the Activation Function.mp4

   2 - Important Terminologies.mp4

   3 - The sigmoid function.mp4

   4 - Hyperbolic tangent function.mp4

   5 - Softmax function.mp4

   6 - Rectified Linear Unit (ReLU) function.mp4

   7 - Leaky Rectified Linear Unit function.mp4

  6 - Gradient Descent Algorithm

   1 - What is Gradient Decent.mp4

   2 - What is Stochastic Gradient Decent.mp4

   3 - Gradient Decent vs Stochastic Gradient Decent.mp4

  7 - Summary Overview of Neural Networks

   1 - How artificial neural networks work.mp4

   2 - Advantages of Neural Networks.mp4

   3 - Disadvantages of Neural Networks.mp4

   4 - Applications of Neural Networks.mp4

  8 - Implementation of ANN in Python

   1 - Introduction.mp4

   10 - Feature scaling.mp4

   11 - Building the Artificial Neural Network.mp4

   12 - Adding the input layer and the first hidden layer.mp4

   13 - Adding the next hidden layer.mp4

   14 - Adding the output layer.mp4

   15 - Compiling the artificial neural network.mp4

   16 - Fitting the ANN model to the training set.mp4

   17 - Predicting the test set results.mp4

   2 - Exploring the dataset.mp4

   3 - Problem Statement.mp4

   4 - Data Pre-processing.mp4

   5 - Loading the dataset.mp4

   6 - Splitting the dataset into independent and dependent variables.mp4

   7 - Label encoding using scikit-learn.mp4

   8 - One-hot encoding using scikit-learn.mp4

   9 - Training and Test Sets Splitting Data.mp4

  9 - Convolutional Neural Networks (CNN)

   1 - Introduction.mp4

   2 - Components of convolutional neural networks.mp4

   3 - Convolution Layer.mp4

   4 - Pooling Layer.mp4

   5 - Fully connected Layer.mp4

  Bonus Resources.txt