NMF, görüntülerin parçalarını öğrenir

Şimdi NMF hakkında öğrendiklerini kullanarak digits veri kümesini ayrıştır. Yine, rakam görüntüleri 2B bir dizi samples olarak veriliyor. Bu kez sana ayrıca herhangi bir 1B dizinin kodladığı görüntüyü gösteren bir show_as_image() fonksiyonu da sağlanıyor:

def show_as_image(sample):
    bitmap = sample.reshape((13, 8))
    plt.figure()
    plt.imshow(bitmap, cmap='gray', interpolation='nearest')
    plt.colorbar()
    plt.show()

İşin bittiğinde, grafiklere bir göz at ve NMF'in rakamı bileşenlerin toplamı olarak nasıl ifade ettiğine dikkat et!

Bu egzersiz

Python'da Unsupervised Learning

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

sklearn.decomposition içinden NMF'i içe aktar.
7 bileşenli bir NMF örneği oluştur ve adını model koy. (7, bir LED ekrandaki hücre sayısıdır).
model'in .fit_transform() metodunu samples üzerinde uygula. Sonucu features değişkenine ata.
Modelin her bir bileşenine (model.components_ üzerinden erişilir) döngü içinde show_as_image() fonksiyonunu uygula.
features'ın 0 numaralı satırını digit_features değişkenine ata.
digit_features'ı yazdır.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Import NMF
____

# Create an NMF model: model
model = ____

# Apply fit_transform to samples: features
features = ____

# Call show_as_image on each component
for component in model.components_:
    ____

# Select the 0th row of features: digit_features
digit_features = ____

# Print digit_features
print(digit_features)

Kodu Düzenle ve Çalıştır

Bu egzersiz

Python'da Unsupervised Learning

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

Learn how to discover the underlying groups (or "clusters") in a dataset. By the end of this chapter, you'll be clustering companies using their stock market prices, and distinguishing different species by clustering their measurements.

Exercise 1: Unsupervised Learning Exercise 2: How many clusters?Exercise 3: Clustering 2D points Exercise 4: Inspect your clustering Exercise 5: Evaluating a clustering Exercise 6: How many clusters of grain?Exercise 7: Evaluating the grain clustering Exercise 8: Transforming features for better clusterings Exercise 9: Scaling fish data for clustering Exercise 10: Clustering the fish data Exercise 11: Clustering stocks using KMeans Exercise 12: Which stocks move together?

In this chapter, you'll learn about two unsupervised learning techniques for data visualization, hierarchical clustering and t-SNE. Hierarchical clustering merges the data samples into ever-coarser clusters, yielding a tree visualization of the resulting cluster hierarchy. t-SNE maps the data samples into 2d space so that the proximity of the samples to one another can be visualized.

Exercise 1: Visualizing hierarchies Exercise 2: How many merges?Exercise 3: Hierarchical clustering of the grain data Exercise 4: Hierarchies of stocks Exercise 5: Cluster labels in hierarchical clustering Exercise 6: Which clusters are closest?Exercise 7: Different linkage, different hierarchical clustering!Exercise 8: Intermediate clusterings Exercise 9: Extracting the cluster labels Exercise 10: t-SNE for 2-dimensional maps Exercise 11: t-SNE visualization of grain dataset Exercise 12: A t-SNE map of the stock market

Dimension reduction summarizes a dataset using its common occuring patterns. In this chapter, you'll learn about the most fundamental of dimension reduction techniques, "Principal Component Analysis" ("PCA"). PCA is often used before supervised learning to improve model performance and generalization. It can also be useful for unsupervised learning. For example, you'll employ a variant of PCA will allow you to cluster Wikipedia articles by their content!

Exercise 1: Visualizing the PCA transformation Exercise 2: Correlated data in nature Exercise 3: Decorrelating the grain measurements with PCA Exercise 4: Principal components Exercise 5: Intrinsic dimension Exercise 6: The first principal component Exercise 7: Variance of the PCA features Exercise 8: Intrinsic dimension of the fish data Exercise 9: Dimension reduction with PCA Exercise 10: Dimension reduction of the fish measurements Exercise 11: A tf-idf word-frequency array Exercise 12: Clustering Wikipedia part I Exercise 13: Clustering Wikipedia part II

In this chapter, you'll learn about a dimension reduction technique called "Non-negative matrix factorization" ("NMF") that expresses samples as combinations of interpretable parts. For example, it expresses documents as combinations of topics, and images in terms of commonly occurring visual patterns. You'll also learn to use NMF to build recommender systems that can find you similar articles to read, or musical artists that match your listening history!

Exercise 1: Negatif olmayan matris ayrıştırması (NMF)Exercise 2: Negatif olmayan veriler Exercise 3: Wikipedia makalelerine NMF uygulamak Exercise 4: Wikipedia makalelerinin NMF özellikleri Exercise 5: NMF örnekleri yeniden oluşturur Exercise 6: NMF yorumlanabilir parçaları öğrenir Exercise 7: NMF, belgelerin konularını öğrenir Exercise 8: LED rakamları veri kümesini keşfet Exercise 9: NMF, görüntülerin parçalarını öğrenir

Geçerli Egzersiz

Exercise 10: PCA parçaları öğrenmez Exercise 11: NMF kullanarak öneri sistemleri oluşturma Exercise 12: 'Cristiano Ronaldo'ya benzer hangi makaleler var?Exercise 13: Müzik sanatçıları önerisi I Exercise 14: Müzik sanatçıları önerisi bölüm II Exercise 15: Son düşünceler