Udemy - Unsupervised Machine Learning Hidden Markov Models in Pyt


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Udemy - Unsupervised Machine Learning Hidden Markov Models in Pyt
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Torrent File Content (51 files)


[DesireCourse.Com] Udemy - Unsupervised Machine Learning Hidden Markov Models in Python
    01 Introduction and Outline
          001 Introduction and Outline Why would you want to use an HMM.mp4 -
6.78 MB

          002 Unsupervised or Supervised.mp4 -
5.27 MB

          003 Where to get the Code and Data.mp4 -
2.09 MB

          004 How to Succeed in this Course.mp4 -
8.78 MB

    02 Markov Models
          005 The Markov Property.mp4 -
8.31 MB

          006 Markov Models.mp4 -
8.17 MB

          007 The Math of Markov Chains.mp4 -
9.04 MB

    03 Markov Models Example Problems and Applications
          008 Example Problem Sick or Healthy.mp4 -
5.54 MB

          009 Example Problem Expected number of continuously sick days.mp4 -
4.63 MB

          010 Example application SEO and Bounce Rate Optimization.mp4 -
15.82 MB

          011 Example Application Build a 2nd-order language model and generate phrases.mp4 -
26.93 MB

          012 Example Application Googles PageRank algorithm.mp4 -
8.72 MB

    04 Hidden Markov Models for Discrete Observations
          013 From Markov Models to Hidden Markov Models.mp4 -
10.17 MB

          014 HMMs are Doubly Embedded.mp4 -
3.14 MB

          015 How can we choose the number of hidden states.mp4 -
7.34 MB

          016 The Forward-Backward Algorithm.mp4 -
6.78 MB

          017 Visual Intuition for the Forward Algorithm.mp4 -
6.03 MB

          018 The Viterbi Algorithm.mp4 -
5.03 MB

          019 Visual Intuition for the Viterbi Algorithm.mp4 -
5.73 MB

          020 The Baum-Welch Algorithm.mp4 -
4.35 MB

          021 Baum-Welch Explanation and Intuition.mp4 -
11.96 MB

          022 Baum-Welch Updates for Multiple Observations.mp4 -
7.48 MB

          023 Discrete HMM in Code.mp4 -
47.42 MB

          024 The underflow problem and how to solve it.mp4 -
7.65 MB

          025 Discrete HMM Updates in Code with Scaling.mp4 -
29.14 MB

          026 Scaled Viterbi Algorithm in Log Space.mp4 -
9.23 MB

    05 Discrete HMMs Using Deep Learning Libraries
          027 Gradient Descent Tutorial.mp4 -
8.43 MB

          028 Theano Scan Tutorial.mp4 -
23.76 MB

          029 Discrete HMM in Theano.mp4 -
30.74 MB

          030 Improving our Gradient Descent-Based HMM.mp4 -
8 MB

          031 Tensorflow Scan Tutorial.mp4 -
23.07 MB

          032 Discrete HMM in Tensorflow.mp4 -
16.44 MB

    06 HMMs for Continuous Observations
          033 Gaussian Mixture Models with Hidden Markov Models.mp4 -
6.27 MB

          034 Generating Data from a Real-Valued HMM.mp4 -
14.94 MB

          035 Continuous-Observation HMM in Code part 1.mp4 -
46.69 MB

          036 Continuous-Observation HMM in Code part 2.mp4 -
15.28 MB

          037 Continuous HMM in Theano.mp4 -
45.41 MB

          038 Continuous HMM in Tensorflow.mp4 -
22.45 MB

    07 HMMs for Classification
          039 Generative vs. Discriminative Classifiers.mp4 -
4.12 MB

          040 HMM Classification on Poetry Data Robert Frost vs. Edgar Allan Poe.mp4 -
24.39 MB

    08 Bonus Example Parts-of-Speech Tagging
          041 Parts-of-Speech Tagging Concepts.mp4 -
8.51 MB

          042 POS Tagging with an HMM.mp4 -
14.38 MB

    09 Appendix
          043 Review of Gaussian Mixture Models.mp4 -
4.99 MB

          044 Theano Tutorial.mp4 -
19.86 MB

          045 Tensorflow Tutorial.mp4 -
13.88 MB

          046 How to install Numpy Scipy Matplotlib Pandas IPython Theano and TensorFlow.mp4 -
43.92 MB

          047 How to Code by Yourself part 1.mp4 -
24.53 MB

          048 How to Code by Yourself part 2.mp4 -
14.8 MB

          049 BONUS Where to get Udemy coupons and FREE deep learning material.mp4 -
4.02 MB

     [DesireCourse.Com].txt -
754 bytes



Description



Unsupervised Machine Learning Hidden Markov Models in Python

HMMs for stock price analysis, language modeling, web analytics, biology, and PageRank.

For More Courses Visit: https://desirecourse.com


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