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    3 Days Live Virtual Training on AI and Deep Learning with Python

    BY
    Simpliv Learning

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    Mode

    Online

    Duration

    3 Days

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study, Virtual Classroom
    Mode of Delivery Video and Text Based
    Frequency of Classes Weekdays

    Course and certificate fees

    certificate availability

    Yes

    certificate providing authority

    Simpliv Learning

    The syllabus

    Introduction to Python

    Introduction to Logistic Regression

    Introduction to Artificial Neural Network

    • History of Neural networks and Deep Learning  
    • How do Biological Neurons work?  
    • Growth of biological neural networks  
    • Diagrammatic representation: Logistic Regression and Perceptron  
    • Multi-Layered Perceptron (MLP)  
    • Notation  
    • Training a single-neuron model  
    • Training an MLP: Chain Rule  
    • Training an MLP: Memoization  
    • Backpropagation  
    • Activation functions  
    • Vanishing Gradient problem  
    • Bias-Variance tradeoff  

    Deep Multi-layer perceptrons

    • Deep Multi-layer perceptrons:1980s to 2010s  
    • Dropout layers & Regularization  
    • Rectified Linear Units (ReLU)  
    • Weight initialization  
    • Batch Normalization  
    • Optimizers: Hill-descent analogy in 2D  
    • Optimizers: Hill descent in 3D and contours  
    • SGD Recap  
    • Batch SGD with momentum  
    • Nesterov Accelerated Gradient (NAG)  
    • Optimizers: AdaGrad  
    • Optimizers : Adadelta andRMSProp  
    • Adam  
    • Which algorithm to choose when?  
    • Gradient Checking and clipping  
    • Softmax and Cross-entropy for multi-class classification  
    • How to train a Deep MLP? 

    Convolutional Neural Network

    • Biological inspiration: Visual Cortex  
    • Convolution: Edge Detection on images  
    • Convolution: Padding and strides  
    • Convolution over RGB images  
    • Convolutional layer  
    • Max-pooling  
    • CNN Training: Optimization  
    • Receptive Fields and Effective Receptive Fields  
    • ImageNet dataset  
    • Data Augmentation  
    • Convolution Layers in Keras  
    • AlexNet  
    • VGGNet  
    • Residual Network  
    • Inception Network  
    • What is Transfer learning?

    Recurrent Neural Network

    • Why RNNs?  
    • Recurrent Neural Network  
    • Training RNNs: Backprop  
    • Types of RNNs  
    • Need for LSTM/GRU  
    • LSTM  
    • GRUs  
    • Deep RNN  
    • Bidirectional RNN

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