What Learning Looks Like: Mediated Learning in Theory and Practice, K-6

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Deep learning reading list. Academic research papers on topics such as reinforcement learning, computer vision, NLP and speech, unwrapping factor, transfer learning, practical method, sparse coding, basic theory, feed forward network, large scale deep learning, circulation network, hyper parameter Full list optimized unsupervised feature learning representative research: Yoshua Bengio, Aaron Courville and Pascal Vincent Review and a new perspective (2012). This article outlines recent research in the field of unsupervised feature learning and deep learning, including probabilistic models, auto encoders, manifold learning, and advances in deep networks.

K-Means is a general unsupervised learning classification algorithm that is commonly used to solve clustering problems. "K" refers to the number of clusters entered by the user. The algorithm starts at randomly chosen points, then optimizes the cluster using the distance formula and finds the best data point grouping. Data scientists ultimately choose the correct "K" value. You know the drill, please check the video for details. PCA is a dimension reduction algorithm that can do something for data scientists. The most important thing is that you can greatly reduce the complexity of your model when dealing with hundreds or thousands of different functions. Although not monitored, users need to analyze the results to maintain approximately 95% of the original dataset's behavior. There are still a lot of problems the PCA needs to solve, so please check the video for details.