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Python
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SQL
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Son Yorumlar

Activation Layer, Pooling Layer

Kategori: Deep Learning, 03 Nisan 2020
Activation Layer, Pooling Layer

Convolution işlemlerinden sonra oluşan outputlar yani feature mapler bir sonraki katmana geçmeden önce aktivasyon fonksiyonundan geçerler. Genellikle ReLu kullanılır. Bir örnek yapalım:

 2 1 -5 2 0 4 -5 3 -1 
Bu ... Devamını Oku

JanFranco | 1 | 0 | 2 min read

Depth, Stride, Padding

Kategori: Deep Learning, 02 Nisan 2020
Depth, Stride, Padding

Convolution işleminde fark ettiğimiz üzere, input resmimiz ile output resmimizin boyutları bir değildir. Bu yazımda boyutlar hakkında konuşacağız ve depth, stride, padding kavramlarından bahsedeceğiz.

Depth kavramı, kullandığımız filtre sayısıdır. Örneğin input resim üzerinde 12 ... Devamını Oku

JanFranco | 2 | 0 | 3 min read

Convolutions, Image Features

Kategori: Deep Learning, 01 Nisan 2020
Convolutions, Image Features

Featurelar, resimdeki ilgi çekici kısımlardır. Bu kısımlar kenarlar, köşeler, patternler olabilir. Convolution işlemi basit olarak şöyle özetlenebilir, bir input görüntü ve bir filtremiz var. Bu filtremiz, input image üzerinde geziniyor ve bir output elde ediliyor. ... Devamını Oku

JanFranco | 3 | 0 | 4 min read

Convolutional Neural Networks - Introduction

Kategori: Deep Learning, 31 Mart 2020
Convolutional Neural Networks - Introduction

Evrişimsel sinir ağları, yapay sinir ağlarının bir alt dalıdır. Bu sinir ağ modelleri, görüntüleri input alır ve görüntüler üzerinde çalışır. Yapay sinir ağı modelleri ile de resimler üzerinde çalışabilirdik ancak bu çok etkisiz bir yöntem ... Devamını Oku

JanFranco | 4 | 0 | 1 min read

Epoch, Batch Size, Iteration

Kategori: Deep Learning, 30 Mart 2020
Epoch, Batch Size, Iteration

Forward propagation ve back propagation işlemlerinin ne olduğunu ve ne işe yaradığını gördük. Training işleminin nasıl yapıldığını temel düzeyde anladık. Son olarak bazı kavramlardan bahsedeceğiz: Epoch, Batch Size, Iterations

Nöral ağ modelinin bir kez ... Devamını Oku

JanFranco | 6 | 0 | 1 min read

Back Propagation

Kategori: Deep Learning, 29 Mart 2020
Back Propagation

Back propagation işleminde, ağırlıklar ve bias değerleri iyileştirilmeye çalışılır. Sondan yani output layerdan başlanır ve input layera doğru işlem devam eder. Amaç bileşenlerin değerini değiştirmek ve bu değişimin sonuca nasıl yansıdığına bakmaktır. Olumlu yansıyor ise ... Devamını Oku

JanFranco | 7 | 0 | 2 min read

Loss Functions

Kategori: Deep Learning, 28 Mart 2020
Loss Functions

Forward propagation işlemini anlatırken output layerda hesaplanan değer ile olması gereken değerlerin karşılaştırıldığından ve farklı karşılaştırma yolları olduğundan bahsetmiştik. Bu yazımda bunları inceleyeceğiz. Aşağıda yaygın kullanılan Loss fonksiyonları listelenmiştir:

L1
L2
Cross Entropy
Hinge ... Devamını Oku

JanFranco | 8 | 0 | 1 min read

Activation Functions

Kategori: Deep Learning, 27 Mart 2020
Activation Functions

Daha önceki dökümanda aktivasyon fonksiyonlarının ne olduğundan ve ne işe yaradığından bahsetmiştik. Bu dökümanda ise aktivasyon fonksiyonunun türlerinden bahseceğiz. En çok kullanılan aktivasyon fonksiyonları:

Sigmoid Activation Function



Tanh Activation Function ... Devamını Oku

JanFranco | 6 | 0 | 1 min read

Forward Propagation

Kategori: Deep Learning, 25 Mart 2020
Forward Propagation

Nöral ağların inputları alıp, output ürettikleri sürece forward propagation denir. Forward propagation işlemi gayet anlaşılır ve basittir.



Resimden de görüleceği gibi, her bileşen arasında bir ağırlık değeri vardır. Buna weight denir. ... Devamını Oku

JanFranco | 11 | 0 | 2 min read

Neural Networks - Introduction

Kategori: Deep Learning, 24 Mart 2020
Neural Networks - Introduction

- Nöral ağlar nedir?

Nöral ağlar bir machine learning algoritmasıdır. Diğer algoritmalar gibi input alır ve output tahmini yapar. Diğer algoritmalardan daha iyi sonuç vermesinin, çalışmasının sebebi non-linear çalışmasıdır. 

Input Layer    Hidden Layer        Output ... Devamını Oku

JanFranco | 12 | 0 | 2 min read