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[CourseClub.ME] AppliedAICourse - Applied Machine Learning Course

Torrent Hash :
470b1ff5490a3567307a728db3fdc0360f484fd8
Content Size :
25.37 GB
Date :
2019-06-01
Short Magnet :
Short Magnet
https://0mag.top/!aMm4B6 QR code
Files ( 1000+ )size
1.1 - How to Learn from Appliedaicourse/1.1 - How to Learn from Appliedaicourse.mp4443.51 MB
34.2 - Productionization and deployment of Machine Learning Models/34.2 - Productionization and deployment of Machine Learning Models.mp4.mkv267.27 MB
1.2 - How the Job Guarantee program works/1.2 - How the Job Guarantee program works.mp4243.85 MB
5.1 - Numpy Introduction/5.1 - Numpy Introduction.mp4157.08 MB
5.2 - Numerical operations on Numpy/5.2 - Numerical operations on Numpy.mp4156.02 MB
45.9 - Univariate AnalysisGene feature/45.9 - Univariate AnalysisGene feature.mp4144.17 MB
3.1 - Lists/3.1 - Lists.mp4141.28 MB
49.6 - Softmax Classifier on MNIST dataset/49.6 - Softmax Classifier on MNIST dataset..mp4140.09 MB
57.26 - Data Control Language GRANT, REVOKE/57.26 - Data Control Language GRANT, REVOKE.mp4138.66 MB
51.6 - LSTM/51.6 - LSTM..mp4137.09 MB
54.4 - Char-RNN with abc-notation Data preparation/54.4 - Char-RNN with abc-notation Data preparation..mp4131.69 MB
41.9 - EDA Advanced Feature Extraction/41.9 - EDA Advanced Feature Extraction.mp4131.34 MB
51.10 - Code example IMDB Sentiment classification/51.10 - Code example IMDB Sentiment classification.mp4122.78 MB
23.5 - Naive Bayes algorithm/23.5 - Naive Bayes algorithm.mp4116.76 MB
42.13 - Code for bag of words based product similarity/42.13 - Code for bag of words based product similarity.mp4116.3 MB
50.2 - ConvolutionEdge Detection on images/50.2 - ConvolutionEdge Detection on images..mp4115.95 MB
23.6 - Toy example Train and test stages/23.6 - Toy example Train and test stages.mp4115.88 MB
45.13 - Baseline Model Naive Bayes/45.13 - Baseline Model Naive Bayes.mp4115.39 MB
53.12 - Test and visualize the output/53.12 - Test and visualize the output..mp4113.79 MB
17.1 - Dataset overview Amazon Fine Food reviews(EDA)/17.1 - Dataset overview Amazon Fine Food reviews(EDA).mp4111 MB
50.14 - Residual Network/50.14 - Residual Network..mp4108.53 MB
24.16 - Code sample Logistic regression, GridSearchCV, RandomSearchCV/24.16 - Code sample Logistic regression, GridSearchCV, RandomSearchCV.mp4106.88 MB
51.2 - Recurrent Neural Network/51.2 - Recurrent Neural Network..mp4105.18 MB
53.10 - NVIDIA’s end to end CNN model/53.10 - NVIDIA’s end to end CNN model..mp4103.55 MB
47.8 - Training an MLP Chain Rule/47.8 - Training an MLP Chain Rule.mp4102.08 MB
48.3 - Rectified Linear Units (ReLU)/48.3 - Rectified Linear Units (ReLU)..mp4102.05 MB
11.9 - Q-Q plotHow to test if a random variable is normally distributed or not/11.9 - Q-Q plotHow to test if a random variable is normally distributed or not.mp4101.62 MB
48.18 - Auto Encoders/48.18 - Auto Encoders..mp497.55 MB
4.2 - Types of functions/4.2 - Types of functions.mp496.07 MB
18.27 - LSH for cosine similarity/18.27 - LSH for cosine similarity.mp496 MB
18.30 - Code SampleDecision boundary/18.30 - Code SampleDecision boundary ..mp495.52 MB
20.17 - curse of dimensionality/20.17 - curse of dimensionality.mp494.95 MB
49.8 - Model 1 Sigmoid activation/49.8 - Model 1 Sigmoid activation.mp494.95 MB
42.6 - Data cleaning and understandingMissing data in various features/42.6 - Data cleaning and understandingMissing data in various features.mp494.76 MB
4.8 - File Handling/4.8 - File Handling.mp492.9 MB
32.16 - Stacking models/32.16 - Stacking models.mp492.87 MB
36.3 - Proximity methods Advantages and Limitations/36.3 - Proximity methods Advantages and Limitations..mp491.85 MB
57.20 - Sub QueriesNested QueriesInner Queries/57.20 - Sub QueriesNested QueriesInner Queries.mp490.52 MB
7.3 - Key Operations on Data Frames/7.3 - Key Operations on Data Frames.mp490.37 MB
24.2 - Sigmoid function Squashing/24.2 - Sigmoid function Squashing.mp490.08 MB
57.13 - Logical Operators/57.13 - Logical Operators.mp488.27 MB
17.5 - Text Preprocessing Stemming/Stop-word removal, Tokenization, Lemmatization (Featurizations - convert text to numeric vectors).mp488.26 MB
54.3 - Char-RNN with abc-notation Char-RNN model/54.3 - Char-RNN with abc-notation Char-RNN model.mp486.88 MB
20.11 - Local outlier Factor(A)/20.11 - Local outlier Factor(A).mp486.75 MB
49.12 - MNIST classification in Keras/49.12 - MNIST classification in Keras..mp486.71 MB
48.16 - Softmax and Cross-entropy for multi-class classification/48.16 - Softmax and Cross-entropy for multi-class classification..mp485.89 MB
14.9 - PCA Code example/14.9 - PCA Code example.mp485.47 MB
48.9 - Batch SGD with momentum/48.9 - Batch SGD with momentum..mp485.03 MB
20.18 - Bias-Variance tradeoff/20.18 - Bias-Variance tradeoff.mp484.12 MB
38.1 - Problem formulation Movie reviews/38.1 - Problem formulation Movie reviews.mp484.02 MB

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