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[ FreeCourseWeb.com ] Udemy - Real World Data Science and Machine Learning Projects.zip 1.9 GB

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06 Appendix/031 How to install Numpy Scipy Matplotlib Pandas IPython Theano and TensorFlow.mp4 46.0 MB
02 Bias-Variance Trade-Off/008 Polynomial Regression Demo.mp4 43.8 MB
06 Appendix/032 How to Code by Yourself part 1.mp4 25.7 MB
03 Bootstrap Estimates and Bagging/015 Bagging Classification Trees.mp4 21.3 MB
03 Bootstrap Estimates and Bagging/014 Bagging Regression Trees.mp4 16.6 MB
06 Appendix/030 Confidence Intervals.mp4 16.6 MB
05 AdaBoost/026 AdaBoost Implementation.mp4 16.5 MB
03 Bootstrap Estimates and Bagging/011 Bootstrap Estimation.mp4 15.9 MB
04 Random Forest/018 Random Forest Regressor.mp4 15.6 MB
06 Appendix/033 How to Code by Yourself part 2.mp4 15.5 MB
04 Random Forest/017 Random Forest Algorithm.mp4 15.1 MB
02 Bias-Variance Trade-Off/009 K-Nearest Neighbor and Decision Tree Demo.mp4 14.5 MB
04 Random Forest/019 Random Forest Classifier.mp4 13.2 MB
05 AdaBoost/025 AdaBoost Loss Function Exponential Loss.mp4 11.7 MB
03 Bootstrap Estimates and Bagging/012 Bootstrap Demo.mp4 11.5 MB
05 AdaBoost/023 AdaBoost Algorithm.mp4 11.4 MB
02 Bias-Variance Trade-Off/005 Bias-Variance Key Terms.mp4 10.7 MB
04 Random Forest/021 Implementing a Not as Random Forest.mp4 9.1 MB
04 Random Forest/020 Random Forest vs Bagging Trees.mp4 8.2 MB
05 AdaBoost/029 Summary and Whats Next.mp4 7.7 MB

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ATKExotics.19.09.17.Iris.Ivy.Sex.Machine.XXX.720p.WEB.x264-GalaXXXy.mkv 164.7 MB
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5 - 4 - Convolutional nets for object recognition [17min].mp4 24.1 MB
7 - 1 - Modeling sequences A brief overview.mp4 21.1 MB
5 - 3 - Convolutional nets for digit recognition [16 min].mp4 19.4 MB
2 - 5 - What perceptrons cant do [15 min].mp4 17.4 MB
8 - 2 - Modeling character strings with multiplicative connections [14 mins].mp4 17.4 MB
8 - 1 - A brief overview of Hessian Free optimization.mp4 17.0 MB
10 - 1 - Why it helps to combine models [13 min].mp4 15.9 MB
6 - 5 - Rmsprop Divide the gradient by a running average of its recent magnitude.mp4 15.9 MB
1 - 1 - Why do we need machine learning [13 min].mp4 15.8 MB
10 - 2 - Mixtures of Experts [13 min].mp4 15.7 MB
6 - 2 - A bag of tricks for mini-batch gradient descent.mp4 15.6 MB
4 - 1 - Learning to predict the next word [13 min].mp4 15.0 MB
4 - 5 - Ways to deal with the large number of possible outputs [15 min].mp4 14.9 MB
8 - 3 - Learning to predict the next character using HF [12 mins].mp4 14.6 MB
9 - 1 - Overview of ways to improve generalization [12 min].mp4 14.2 MB
3 - 1 - Learning the weights of a linear neuron [12 min].mp4 14.2 MB
3 - 4 - The backpropagation algorithm [12 min].mp4 14.0 MB
9 - 5 - The Bayesian interpretation of weight decay [11 min].mp4 12.9 MB
9 - 4 - Introduction to the full Bayesian approach [12 min].mp4 12.6 MB
8 - 4 - Echo State Networks [9 min].mp4 11.8 MB