Veri kümeni hazırlama

Konsolda, etiketlerin darts.competitor ağın tarafından anlaşılabilecek bir biçimde olmadığını görebilirsin. Şu anda yarışmacıların adlarını metin olarak içeriyorlar. Önce bu yarışmacıları benzersiz sayılara dönüştüreceksin, ardından bu sayıları tek-seçmeli (one-hot) gösterime çevirmek için keras.utils içindeki to_categorical() fonksiyonunu kullanacaksın.

Bu, çok sınıflı sınıflandırma problemleri için kullanışlıdır; çünkü çıktı nöron sayısı sınıf sayısına eşittir ve veri kümemizdeki her gözlem için sadece bir nöronun aktif olmasını isteriz.

Dart veri kümesi darts olarak yüklendi. Pandas pd olarak içe aktarıldı. Hadi bu veri kümesini hazırlayalım!

Bu egzersiz

Keras ile Deep Learning'e Giriş

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Transform into a categorical variable
darts.competitor = pd.____(darts.competitor)

# Assign a number to each category (label encoding)
darts.competitor = darts.competitor.____.____ 

# Print the label encoded competitors
print('Label encoded competitors: \n',darts.competitor.head())

Kodu Düzenle ve Çalıştır

Bu egzersiz

Keras ile Deep Learning'e Giriş

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

In this first chapter, you will get introduced to neural networks, understand what kind of problems they can solve, and when to use them. You will also build several networks and save the earth by training a regression model that approximates the orbit of a meteor that is approaching us!

Exercise 1: What is Keras?Exercise 2: Describing Keras Exercise 3: Would you use deep learning?Exercise 4: Your first neural network Exercise 5: Hello nets!Exercise 6: Counting parameters Exercise 7: Build as shown!Exercise 8: Surviving a meteor strike Exercise 9: Specifying a model Exercise 10: Training Exercise 11: Predicting the orbit!

By the end of this chapter, you will know how to solve binary, multi-class, and multi-label problems with neural networks. All of this by solving problems like detecting fake dollar bills, deciding who threw which dart at a board, and building an intelligent system to water your farm. You will also be able to plot model training metrics and to stop training and save your models when they no longer improve.

Exercise 1: İkili sınıflandırma Exercise 2: Dolar banknotlarını keşfet Exercise 3: İkili sınıflandırma modeli Exercise 4: Bu dolar sahte mi?Exercise 5: Çok sınıflı sınıflandırma Exercise 6: Çok sınıflı bir model Exercise 7: Veri kümeni hazırlama

Geçerli Egzersiz

Exercise 8: Dart atanları eğitme Exercise 9: Softmax tahminleri Exercise 10: Çok etiketli sınıflandırma Exercise 11: Bir sulama makinesi Exercise 12: Birden çok etiketle eğitim Exercise 13: Keras geri çağırımları Exercise 14: History geri çağırımı Exercise 15: Modelini erken durdurma Exercise 16: Callback'lerin bir kombinasyonu

In the previous chapters, you've trained a lot of models! You will now learn how to interpret learning curves to understand your models as they train. You will also visualize the effects of activation functions, batch-sizes, and batch-normalization. Finally, you will learn how to perform automatic hyperparameter optimization to your Keras models using sklearn.

Exercise 1: Learning curves Exercise 2: Learning the digits Exercise 3: Is the model overfitting?Exercise 4: Do we need more data?Exercise 5: Activation functions Exercise 6: Different activation functions Exercise 7: Comparing activation functions Exercise 8: Comparing activation functions II Exercise 9: Batch size and batch normalization Exercise 10: Changing batch sizes Exercise 11: Batch normalizing a familiar model Exercise 12: Batch normalization effects Exercise 13: Hyperparameter tuning Exercise 14: Preparing a model for tuning Exercise 15: Tuning the model parameters Exercise 16: Training with cross-validation

It's time to get introduced to more advanced architectures! You will create an autoencoder to reconstruct noisy images, visualize convolutional neural network activations, use deep pre-trained models to classify images and learn more about recurrent neural networks and working with text as you build a network that predicts the next word in a sentence.

Exercise 1: Tensors, layers, and autoencoders Exercise 2: It's a flow of tensors Exercise 3: Neural separation Exercise 4: Building an autoencoder Exercise 5: De-noising like an autoencoder Exercise 6: Intro to CNNs Exercise 7: Building a CNN model Exercise 8: Looking at convolutions Exercise 9: Preparing your input image Exercise 10: Using a real world model Exercise 11: Intro to LSTMs Exercise 12: Text prediction with LSTMs Exercise 13: Build your LSTM model Exercise 14: Decode your predictions Exercise 15: Test your model!Exercise 16: You're done!