Receptive Field Arithmetic for Convolutional Neural Networks

Receptive Field Arithmetic for Convolutional Neural Networks

In the past couple of years, convolutional neural networks became one of the most used deep learning concepts. They are used in a variety of industries for object detection, pose estimation, and image classification. For example, in healthcare, they are heavily used...
Creating Custom TensorFlow Dataset

Creating Custom TensorFlow Dataset

In the previous article, we had a chance to see how one can scrape images from the web using Python. Apart from that, in one of the articles before that we could see how we can perform transfer learning with TensorFlow. In that article, we used famous Convolution...
Transfer Learning with TensorFlow 2 – Model Fine Tuning

Transfer Learning with TensorFlow 2 – Model Fine Tuning

In the previous article, we had a chance to explore transfer learning with TensorFlow 2. We used several huge pre-trained models: VGG16, GoogLeNet and ResNet. These architectures are all trained on ImageNet dataset and their weights are stored. We specialized them for...
Transfer Learning with TensorFlow 2

Transfer Learning with TensorFlow 2

It is always fun and educational to read deep learning scientific papers. Especially if it is in the area of the current project that you are working on. However, often these papers contain architectures and solutions that are hard to train. Especially if you want to...
How to Integrate TensorFlow Model in Angular Application?

How to Integrate TensorFlow Model in Angular Application?

The code that accompanies this article can be downloaded here. Couple of months back we investigated parts of TensorFlow’s ecosystem beyond standard library. To be more precise, we investigated TensorFlow.js and how you can build and train models in the browser...
Deep Convolutional Q-Learning with Python and TensorFlow 2.0

Deep Convolutional Q-Learning with Python and TensorFlow 2.0

So far in our journey through the world of reinforcement learning we covered several topics. First we kicked it off with introduction to reinforcement learning and we saw how this paradigm functions. Then we learned the simplest from of it – Q-Learning. Finally,...