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文档列表

9. Spatiotemporal structure using forward models/4. Example Simulate human brain (EEG) data.mp4 35.4 MB
9. Spatiotemporal structure using forward models/2. Forward model 2D sheet.mp4 32.9 MB
4. Time series signals/3. Smooth transients.mp4 20.8 MB
9. Spatiotemporal structure using forward models/3. Mixed overlapping forward models.mp4 19.3 MB
4. Time series signals/6. Dipolar and multipolar chirps.mp4 16.2 MB
3. Data distributions/2. Normal and uniform distributions.mp4 15.3 MB
5. Time series noise/5. Multivariable correlated noise.mp4 13.9 MB
3. Data distributions/4. Poisson distribution.mp4 13.4 MB
2. Descriptive statistics and basic visualizations/2. Mean, median, standard deviation, variance.mp4 12.9 MB
5. Time series noise/3. Pink noise (aka 1f aka fractal).mp4 12.7 MB
1. Introductions/4. The importance of visualization.mp4 12.0 MB
8. Data clustering in space/2. Clusters in 2D.mp4 11.5 MB
3. Data distributions/3. QQ plot.mp4 11.4 MB
3. Data distributions/7. Cohen_s d for separating distributions.mp4 11.2 MB
7. Image noise/4. Perlin noise in 2D.mp4 10.4 MB
5. Time series noise/2. Seeded reproducible normal and uniform noise.mp4 10.0 MB
6. Image signals/3. Sine patches and Gabor patches.mp4 9.6 MB
8. Data clustering in space/3. Clusters in N-D.mp4 9.4 MB
1. Introductions/2. Why and how to simulate data.mp4 9.3 MB
4. Time series signals/2. Sharp transients.mp4 9.3 MB
2. Descriptive statistics and basic visualizations/5. Violin plot.mp4 9.1 MB
10. How to become a proactive data scientist/3. Write down or sketch the important results.mp4 9.0 MB
7. Image noise/5. Filtered 2D-FFT noise.mp4 8.9 MB
1. Introductions/3. What is signal and what is noise.mp4 8.8 MB
4. Time series signals/4. Repeating sine, square, and triangle waves.mp4 8.7 MB
2. Descriptive statistics and basic visualizations/3. Interquartile range.mp4 8.6 MB
5. Time series noise/4. Brownian noise (aka random walk).mp4 8.4 MB
1. Introductions/1. Overall goals of this course.mp4 8.2 MB
6. Image signals/4. Geometric shapes.mp4 7.6 MB
3. Data distributions/6. Measures of distribution quality (SNR and Fano factor).mp4 7.0 MB
6. Image signals/2. Lines and edges.mp4 6.8 MB
2. Descriptive statistics and basic visualizations/4. Histogram.mp4 6.7 MB
3. Data distributions/5. Log-normal distribution.mp4 6.6 MB
10. How to become a proactive data scientist/1. Proactive vs. reactive data science.mp4 6.5 MB
4. Time series signals/5. Multicomponent oscillators.mp4 6.5 MB
10. How to become a proactive data scientist/2. Understand data origins and features.mp4 5.6 MB
7. Image noise/3. Checkerboard patterns and noise.mp4 5.4 MB
7. Image noise/2. Image white noise.mp4 5.3 MB
11. Conclusions and how to learn more/1. Conclusions and how to learn more.mp4 5.2 MB
10. How to become a proactive data scientist/4. Don_t give up -- every mistake is a learning opportunity!.mp4 4.9 MB
9. Spatiotemporal structure using forward models/1.1 prodata_forwardModels.zip.zip 4.4 MB
6. Image signals/5. Rings.mp4 4.0 MB
7. Image noise/1.1 prodata_imageNoise.zip.zip 669.9 kB
4. Time series signals/1.1 prodata_TimeSeriesSignals.zip.zip 668.7 kB
5. Time series noise/1.1 prodata_TimeSeriesNoise.zip.zip 485.5 kB
3. Data distributions/1.1 prodata_dataDistributions.zip.zip 312.5 kB
8. Data clustering in space/1.1 prodata_dataClusters.zip.zip 285.8 kB
6. Image signals/1.1 prodata_imageSignals.zip.zip 269.9 kB
2. Descriptive statistics and basic visualizations/1.1 prodata_descriptiveVisualizations.zip.zip 243.0 kB
Discuss.FreeTutorials.Us.html 169.7 kB
FreeCoursesOnline.Me.html 110.9 kB
FreeTutorials.Eu.html 104.7 kB
12. Thanks! and coupon for other courses/1.1 THANKS.pdf.pdf 103.0 kB
9. Spatiotemporal structure using forward models/4. Example Simulate human brain (EEG) data.vtt 15.9 kB
4. Time series signals/3. Smooth transients.vtt 12.0 kB
9. Spatiotemporal structure using forward models/2. Forward model 2D sheet.vtt 9.5 kB
4. Time series signals/6. Dipolar and multipolar chirps.vtt 8.8 kB
3. Data distributions/2. Normal and uniform distributions.vtt 8.7 kB
2. Descriptive statistics and basic visualizations/2. Mean, median, standard deviation, variance.vtt 8.4 kB
1. Introductions/4. The importance of visualization.vtt 8.2 kB
5. Time series noise/5. Multivariable correlated noise.vtt 8.1 kB
3. Data distributions/3. QQ plot.vtt 7.3 kB
3. Data distributions/4. Poisson distribution.vtt 7.2 kB
3. Data distributions/7. Cohen_s d for separating distributions.vtt 6.8 kB
8. Data clustering in space/2. Clusters in 2D.vtt 6.7 kB
5. Time series noise/3. Pink noise (aka 1f aka fractal).vtt 6.7 kB
1. Introductions/2. Why and how to simulate data.vtt 6.5 kB
2. Descriptive statistics and basic visualizations/5. Violin plot.vtt 5.8 kB
5. Time series noise/2. Seeded reproducible normal and uniform noise.vtt 5.4 kB
4. Time series signals/2. Sharp transients.vtt 5.3 kB
10. How to become a proactive data scientist/3. Write down or sketch the important results.vtt 5.3 kB
9. Spatiotemporal structure using forward models/3. Mixed overlapping forward models.vtt 5.1 kB
6. Image signals/3. Sine patches and Gabor patches.vtt 5.0 kB
7. Image noise/4. Perlin noise in 2D.vtt 4.7 kB
1. Introductions/1. Overall goals of this course.vtt 4.7 kB
5. Time series noise/4. Brownian noise (aka random walk).vtt 4.7 kB
2. Descriptive statistics and basic visualizations/3. Interquartile range.vtt 4.6 kB
3. Data distributions/6. Measures of distribution quality (SNR and Fano factor).vtt 4.5 kB
10. How to become a proactive data scientist/1. Proactive vs. reactive data science.vtt 4.5 kB
10. How to become a proactive data scientist/2. Understand data origins and features.vtt 4.5 kB
7. Image noise/5. Filtered 2D-FFT noise.vtt 4.1 kB
1. Introductions/3. What is signal and what is noise.vtt 4.0 kB
4. Time series signals/4. Repeating sine, square, and triangle waves.vtt 4.0 kB
3. Data distributions/5. Log-normal distribution.vtt 4.0 kB
2. Descriptive statistics and basic visualizations/4. Histogram.vtt 4.0 kB
6. Image signals/2. Lines and edges.vtt 3.8 kB
4. Time series signals/5. Multicomponent oscillators.vtt 3.7 kB
6. Image signals/4. Geometric shapes.vtt 3.5 kB
7. Image noise/3. Checkerboard patterns and noise.vtt 3.3 kB
11. Conclusions and how to learn more/1. Conclusions and how to learn more.vtt 3.2 kB
6. Image signals/5. Rings.vtt 2.9 kB
7. Image noise/2. Image white noise.vtt 2.9 kB
10. How to become a proactive data scientist/4. Don_t give up -- every mistake is a learning opportunity!.vtt 2.7 kB
8. Data clustering in space/3. Clusters in N-D.vtt 2.1 kB
[TGx]Downloaded from torrentgalaxy.org.txt 524 Bytes
How you can help Team-FTU.txt 259 Bytes
9. Spatiotemporal structure using forward models/1. Course materials for this section (reader, MATLAB code, Python code).html 116 Bytes
Torrent Downloaded From GloDls.to.txt 84 Bytes
3. Data distributions/1. Course materials for this section (reader, MATLAB code, Python code).html 76 Bytes
8. Data clustering in space/1. Course materials for this section (reader, MATLAB code, Python code).html 73 Bytes
7. Image noise/1. Course materials for this section (reader, MATLAB code, Python code).html 72 Bytes
6. Image signals/1. Course materials for this section (reader, MATLAB code, Python code).html 70 Bytes
5. Time series noise/1. Course materials for this section (reader, MATLAB code, Python code).html 69 Bytes
2. Descriptive statistics and basic visualizations/1. Course materials for this section (reader, MATLAB code, Python code).html 68 Bytes
4. Time series signals/1. Course materials for this section (reader, MATLAB code, Python code).html 66 Bytes
12. Thanks! and coupon for other courses/1. Thanks and coupon for other courses.html 58 Bytes

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