Deep Learning Courses Syllabus

Deep Learning Course Syllabus

Introduction to Neural Network
  • what is neural network..?
  • How neural networks works?
  • Gradient descent
  • Stochastic Gradient descent
  • Perceptron
  • Multilayer Perceptron
  • BackPropagation
Building Deep learning Environment
  • Overview of deep learning
  • DL environment setup locally
    • Installing Tensorflow
    • Installing Keras
  • Setting up a DL environment in the cloud
    • AWS
    • GCP
  • Run Tensorflow program on AWS cloud plateform
Tenserfow Basics
  • Placeholders in Tensorflow
    • Defining placeholders
    • Feeding placeholders with data
  • Variables,
  • Constant
  • Computation graph
  • Visualize graph with Tensor Board
Activation Functions
  • What are activation functions?
  • Sigmoid function
  • Hyperbolic Tangent function
  • ReLu -Rectified Linear units
  • Softmax function
Training Neural Network for MNIST dataset
  • Exploring the MNIST dataset
  • Defining the hyperparameters
  • Model definition
  • Building the training loop
  • Overfitting and Underfitting
  • Building Inference


Word Representation Using word2vec
  • Learning word vectors
    • Loading all dependencies
    • Preparing the text corpus
    • defining our word2vec model
    • Training the model
    • Analyzing the model
  • Visualizing the embedding space by plotting the model on tensorboard
Clasifying Images with Convolutional Neural Networks(CNN)
  • Introduction to CNN
  • Train a simple convolutional neural net
  • Pooling layer in CNN
  • Building ,training and evaluating our first CNN
  • Model performance optimization
Popular CNN Model Architectures
  • Introduction to Imagenet
  • LeNet architecture
  • AlexNet architecture
  • VGGNet architecture
  • ResNet architecture
Introduction to Recurrent Neural Networks(RNN)
  • What are Recurrent Neural Networks (RNNs)?
  • Understanding a Recurrent Neuron in Detail
  • Long Short-Term Memory(LSTM)
  • Back propagation Through Time(BPTT)
  • Implementation of RNN in Keras
Sequence-to-Sequence Models for Building Chatbot
HandWritten Digits and letters Classification Using CNN
  • Code Implementation
    • Importing all of the dependencies
    • Defining the hyperparameters
    • Building a simple deep neural network
    • Convolution in keras
    • Pooling
    • Dropout technique
    • Data augmentation

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