Deep Learning

Deep Learning

Deep Learning

Deep learning neural networks used to identify objects and determine optimal actions


    • Why Deep Learning?
    • Quick recap of machine learning concepts
    • What is a neural network?
    • Three reasons to go Deep
    • Your choice of Deep Net
    • An old problem: The Vanishing Gradient
    • Restricted Boltzmann Machines
    • Deep Belief Nets
    • Convolutional Networks
    • Recurrent Nets
  1. NLP

    • Structure extraction
    • Identify and mark sentence, phrase, and paragraph boundaries
    • Language identification
    • Tokenization
    • Acronym normalization and tagging
    • Lemmatization / Stemming
    • Decompounding
    • Entity extraction
    • Bag-of-words model and algorithms for NLP
    • Object Detection
    • Face Recognition
    • Histogram Analysis
    • Project : Traffic Car Count
    • Setting up KERAS
    • Creating a Neural Network
    • Training Models and Monitoring
    • Artificial Neural Networks
    • Neural Networks using Tensorflow
    • Debugging and Monitoring
    • Convolutional Neural Networks
    • Unsupervised Learning
    • Neurons, ANN & Working
    • Single Layer Perceptron Model
    • Multilayer Neural Network
    • Feed Forward Neural Network
    • Cost Function Formation
    • Applying Gradient Descent Algorithm
    • Backpropagation Algorithm & Mathematical Modelling
    • Programming Flow for backpropagation algorithm
    • Use Cases of ANN
    • Programming SLNN using Python
    • Programming MLNN using Python/R
    • Digit Recognition using MLNN
    • XOR Logic using MLNN & Backpropagation
    • Diabetes Data Predictive Analysis using ANN
    • Project – Banking Problem Analysis – When the customer will leave?
    • Project – Medical Problem Analysis


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