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    Artificial Neural Networks 2

    This path introduces you to the mathematical discussion of artificial neural networks and deep learning.

    Curated by Camillo

    Beginner Lvl 1
    8h
    5.3k
    4.6(21,556)

    Calculus Explore / Get started with

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    Top 3
    1. Module
    40

    Backpropagation

    Minimizing the cost function to train deep neural networks

    This operation is inspired by the human brain, yet proved itself successful in training ANNs.

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    Author:    
    Time required: 8h
    Cost: 0€
    40

    Neural Networks and Deep Learning

    Core concepts behind neural networks and deep learning

    This book feeds you with more technicalities on ANNs!

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    Author: Michael Nielsen   
    Time required: 8h
    Cost: 0€
    40

    Calculus on Computational Graphs: Backpropagation

    Colah

    Read this valuable explanation on backpropagation to fully understand the concept.

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    Author: Christopher Olah   
    Time required: 8h
    Cost: 0€
    2. Module
    40

    Activation function

    It defines the output of that node, or "neuron," given an input or set of inputs, it maps the resulting values into the desired range such as between 0 to 1 or -1 to 1

    Check different kinds of activation functions like Sigmoid or ReLu!

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    Author:    
    Time required: 8h
    Cost: 0€
    40

    Neural Networks and Deep Learning

    deeplearning.ai

    Foundations of deep learning

    This course entails valuable explainations worth watching!

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    Author: Andrew Ng   
    Time required: 8h
    Cost: 0€
    40

    Rectified linear unit (ReLU)

    Linear (identity) for all positive values, and zero for all negative values, popular for deep neural networks

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    Author:    
    Time required: 8h
    Cost: 0€
    3. Module
    40

    deeplearning.ai

    deeplearning.ai

    Five-course specialization

    Follow more courses to discover different architectures for deep learning!

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    Author: Andrew Ng   
    Time required: 8h
    Cost: 0€
    40

    Batch normalization

    A technique for improving the speed, performance, and stability of artificial neural networks. It is used to normalize the input layer by adjusting and scaling the activations

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    Author:    
    Time required: 8h
    Cost: 0€
    4. Module
    40

    Deep Learning

    MIT

    An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.

    Detailed technical description of all kinds of concepts in deep learning.

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    Author: Ian Goodfellow Yoshua Bengio   Aaron Courville
    Time required: 8h
    Cost: 0€
    40

    Machine Learning Explained

    Dissecting papers, explanation of the intuition and theory, aggregating resources, computer vision, deep learning, natural language processing

    Articles on advanced ML concepts

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    Author: Keita Kurita   
    Time required: 8h
    Cost: 0€
    40

    Papers With Code

    Curated implementations of machine learning models in pytorch, tensor flow, caffe2, computer vision, natural language processing

    This platform entails papers that present solutions to various machine learning tasks from a theoretical and practical perspective.

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    Author:    
    Time required: 8h
    Cost: 0€
    5. Extras