Farklı aktivasyon fonksiyonları

sigmoid(), tanh(), ReLU() ve leaky_ReLU() fonksiyonları tanımlanmış durumda ve kullanıma hazır. Her fonksiyon bir X girişi alır ve karşılık gelen Y değerini döndürür.

Aşağıdaki ifadelerden hangisi yanlıştır?

Bu egzersiz

Keras ile Deep Learning'e Giriş

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

İnteraktif egzersizlerimizden biriyle teoriyi pratiğe dökün

Egzersizi başlat

Bu egzersiz

Keras ile Deep Learning'e Giriş

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

104 reviews

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: Binary classification Exercise 2: Exploring dollar bills Exercise 3: A binary classification model Exercise 4: Is this dollar bill fake ?Exercise 5: Multi-class classification Exercise 6: A multi-class model Exercise 7: Prepare your dataset Exercise 8: Training on dart throwers Exercise 9: Softmax predictions Exercise 10: Multi-label classification Exercise 11: An irrigation machine Exercise 12: Training with multiple labels Exercise 13: Keras callbacks Exercise 14: The history callback Exercise 15: Early stopping your model Exercise 16: A combination of callbacks

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: Öğrenme eğrileri Exercise 2: Rakamları öğrenmek Exercise 3: Model aşırı öğreniyor mu?Exercise 4: Daha fazla veriye ihtiyacımız var mı?Exercise 5: Aktivasyon fonksiyonları Exercise 6: Farklı aktivasyon fonksiyonları

Geçerli Egzersiz

Exercise 7: Aktivasyon fonksiyonlarını karşılaştırma Exercise 8: Aktivasyon fonksiyonlarını karşılaştırma II Exercise 9: Yığın boyutu ve yığın normalleştirme Exercise 10: Batch boyutlarını değiştirmek Exercise 11: Tanıdık bir modeli batch normalize etme Exercise 12: Batch normalization etkileri Exercise 13: Hiperparametre ayarlama Exercise 14: Ayarlamaya uygun bir model hazırlama Exercise 15: Model parametrelerini ayarlama Exercise 16: Çapraz doğrulama ile eğitim

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!