Perbarui dan simpan

Mungkin Anda sedang melatih model Machine Learning untuk mengenali pemandangan laut. Anda ingin model memahami bahwa laut tidak selalu identik dengan warna cerah musim panas, sehingga Anda juga menyertakan gambar laut saat cuaca buruk atau menjelang malam. Anda mungkin perlu mentransformasi beberapa gambar secara manual untuk menyeimbangkan data, jadi tugas Anda adalah menggelapkan adegan laut Monet dalam rgb_array.

Ingat dari video bahwa putih terkait dengan nilai RGB maksimum 255, sedangkan warna yang lebih gelap terkait dengan nilai yang lebih rendah. numpy sudah dimuat sebagai np, dan rgb_array Monet berdimensi 3D yang Anda muat pada latihan sebelumnya tersedia.

Latihan ini adalah bagian dari kursus

Pengantar NumPy

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# Reduce every value in rgb_array by 50 percent
darker_rgb_array = ____

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Latihan ini adalah bagian dari kursus

Pengantar NumPy

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Mulai Kursus Gratis

Meet the incredible NumPy array! Learn how to create and change array shapes to suit your needs. Finally, discover NumPy's many data types and how they contribute to speedy array operations.

Exercise 1: Introducing arrays Exercise 2: Your first NumPy array Exercise 3: Creating arrays from scratch Exercise 4: A range array Exercise 5: Array dimensionality Exercise 6: 3D array creation Exercise 7: The fourth dimension Exercise 8: Flattening and reshaping Exercise 9: NumPy data types Exercise 10: The dtype argument Exercise 11: Anticipating data types Exercise 12: A smaller sudoku game

Sharpen your NumPy data wrangling skills by slicing, filtering, and sorting New York City’s tree census data. Create new arrays by pulling data based on conditional statements, and add and remove data along any dimension to suit your purpose. Along the way, you’ll learn the shape and dimension compatibility principles to prepare for super-fast array math.

Exercise 1: Indexing and slicing arrays Exercise 2: Slicing and indexing trees Exercise 3: Stepping into 2D Exercise 4: Sorting trees Exercise 5: Filtering arrays Exercise 6: Filtering with masks Exercise 7: Fancy indexing vs. np.where()Exercise 8: Creating arrays from conditions Exercise 9: Adding and removing data Exercise 10: Compatible or not?Exercise 11: Adding rows Exercise 12: Adding columns Exercise 13: Deleting with np.delete()

Leverage NumPy’s speedy vectorized operations to gather summary insights on sales data for American liquor stores, restaurants, and department stores. Vectorize Python functions for use in your NumPy code. Finally, use broadcasting logic to perform mathematical operations between arrays of different sizes.

Exercise 1: Summarizing data Exercise 2: Sales totals Exercise 3: Plotting averages Exercise 4: Cumulative sales Exercise 5: Vectorized operations Exercise 6: Tax calculations Exercise 7: Projecting sales Exercise 8: Vectorizing .upper()Exercise 9: Broadcasting Exercise 10: Broadcastable or not?Exercise 11: Broadcasting across columns Exercise 12: Broadcasting across rows

NumPy meets the art world in this final chapter as we use image data from a Monet masterpiece to explore how you can use to augment image data. You’ll use flipping and transposing functionality to quickly transform our masterpiece. Next, you’ll pull the Monet array apart, make changes, and reconstruct it using array stacking to see the results.

Exercise 1: Menyimpan dan memuat array Exercise 2: Memuat berkas .npy Exercise 3: Mendapatkan bantuan Exercise 4: Perbarui dan simpan

Latihan Saat Ini

Exercise 5: Akrobat array Exercise 6: Augmentasi Monet Exercise 7: Melakukan transpose pada mahakarya Anda Exercise 8: Stacking dan splitting Exercise 9: Pemisahan dan penumpukan 2D Exercise 10: Memisahkan data RGB Exercise 11: Menumpuk data RGB Exercise 12: Selamat!