Published:
In my previous post, I examined the question of how many layers a neural network needs for simple classification tasks. I have explored the linear classification of the AND and OR data sets and the more complex XOR and two-moon data.
Published:
Deep learning is booming. Now with the availability of high-level neural networks API, such as Keras, it is easy to run deep neural networks and solve complex classification problems. Still, from time to time, it is valuable to go back to simple tasks that we can fully understand and this is the aim of this publication.
Published:
In this post, I will quickly solve the classification problem of grayscale images of handwritten digits using Keras. This data set, also called MNIST, is classic. It contains tenths of thousand of handwritten numbers (which are 28x28 pixels), and the classification task is to categorize them into their ten categories: 0 to 9.
Published:
In this post, I will quickly solve the classification problem of grayscale images of handwritten digits using Keras. This data set, also called MNIST, is classic. It contains tenths of thousand of handwritten numbers (which are 28x28 pixels), and the classification task is to categorize them into their ten categories: 0 to 9.
Published:
In my previous post, I examined the question of how many layers a neural network needs for simple classification tasks. I have explored the linear classification of the AND and OR data sets and the more complex XOR and two-moon data.
Published:
Deep learning is booming. Now with the availability of high-level neural networks API, such as Keras, it is easy to run deep neural networks and solve complex classification problems. Still, from time to time, it is valuable to go back to simple tasks that we can fully understand and this is the aim of this publication.
Published:
In this post, I will quickly solve the classification problem of grayscale images of handwritten digits using Keras. This data set, also called MNIST, is classic. It contains tenths of thousand of handwritten numbers (which are 28x28 pixels), and the classification task is to categorize them into their ten categories: 0 to 9.
Published:
In this post, I will quickly solve the classification problem of grayscale images of handwritten digits using Keras. This data set, also called MNIST, is classic. It contains tenths of thousand of handwritten numbers (which are 28x28 pixels), and the classification task is to categorize them into their ten categories: 0 to 9.
Published:
In my previous post, I examined the question of how many layers a neural network needs for simple classification tasks. I have explored the linear classification of the AND and OR data sets and the more complex XOR and two-moon data.
Published:
Deep learning is booming. Now with the availability of high-level neural networks API, such as Keras, it is easy to run deep neural networks and solve complex classification problems. Still, from time to time, it is valuable to go back to simple tasks that we can fully understand and this is the aim of this publication.
Published:
In this post, I will quickly solve the classification problem of grayscale images of handwritten digits using Keras. This data set, also called MNIST, is classic. It contains tenths of thousand of handwritten numbers (which are 28x28 pixels), and the classification task is to categorize them into their ten categories: 0 to 9.
Published:
In my previous post, I examined the question of how many layers a neural network needs for simple classification tasks. I have explored the linear classification of the AND and OR data sets and the more complex XOR and two-moon data.
Published:
In my previous post, I examined the question of how many layers a neural network needs for simple classification tasks. I have explored the linear classification of the AND and OR data sets and the more complex XOR and two-moon data.
Published:
Deep learning is booming. Now with the availability of high-level neural networks API, such as Keras, it is easy to run deep neural networks and solve complex classification problems. Still, from time to time, it is valuable to go back to simple tasks that we can fully understand and this is the aim of this publication.
Published:
Deep learning is booming. Now with the availability of high-level neural networks API, such as Keras, it is easy to run deep neural networks and solve complex classification problems. Still, from time to time, it is valuable to go back to simple tasks that we can fully understand and this is the aim of this publication.