Research Topics in ML & DL

Machine Learning    |    Intermediate
  • 13 Videos | 47m 11s
  • Includes Assessment
  • Earns a Badge
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This course explores research being done in machine learning and deep learning. Topics covered include neural networks and deep neural networks. First, learners examine how to prevent neural networks from overfitting. You will explore research on multilabel learning algorithms, multilabel classification, and multiple-output classifications, which are variants of the standard classification problem. Then examine deep learning algorithms, the enhanced performance of deeper neural networks that are more adept at automatic feature extraction. Next, ut facial alignment, regression tree ensembles, and deep features for scene recognition. Review ELM (Extreme Learning Machine), and how it is used to perform regression and multiclass classification.

WHAT YOU WILL LEARN

  • understand the efforts being undertaken to reduce overfitting using the dropout technique
    understand leading edge multi-label learning algorithms
    understand the proposed learning framework for deep residual learning that improves training of networks that are significantly deeper than traditional neural networks
    understand how initializing a network with transferred features may boost generalization performance
    understand how convolutional neural networks may be utilized as a powerful class of models for image recognition
    understand the dataset that advances state-of-the-art object recognition by considering the context within the question of scene understanding
  • understand the proposed framework for estimating generative models via an adversarial process that successfully estimates the probability that a sample came from training data rather than a generative model
    understand how optimal nearest neighbor algorithms perform compared to traditional nearest neighbor algorithms
    understand how an ensemble of regression trees may successfully estimate facial landmark positions while delivering real-time performance and high quality predictions
    understand how a proposed new scene-centric database is successfully used for learning deep features for scene recognition
    recognize how ELM tends to produce better scalability, generalization performance, and faster learning than traditional support vector machine
    understand the trending research topics in ML and DL

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    2m 31s
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  • Playable
    2. 
    Prevent Neural Networks from Overfitting
    2m 51s
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    3. 
    Multi-Label Learning Algorithms
    3m 56s
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    4. 
    Deep Residual Learning for Image Recognition
    3m 8s
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    5. 
    Transferable Features in Deep Neural Networks
    2m 46s
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    6. 
    Large-Scale Video Classification
    3m 19s
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    7. 
    Common Objects in Context
    3m 11s
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    8. 
    Generative Adversarial Nets
    3m 19s
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    9. 
    Scalable Nearest Neighbor Algorithms
    2m 50s
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    10. 
    Face Alignment with Ensemble of Regression Trees
    2m 4s
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    11. 
    Learning Deep Features for Scene Recognition
    3m 30s
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    12. 
    Extreme Learning Machine (ELM)
    2m 54s
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    13. 
    Exercise: Recognize Research Topics in ML and DL
    5m 24s

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