So far, we have entered several Kaggle’s machine learning competitions. Most of the time, we use Kaggle’s free kernel to solve the puzzles. From the Deal Probability Prediction challenge, we reached the limit of Kaggle’s kernel — the 17 GB RAM limit, while we were training our model. When we added more training data or […]
AWS Tutorial: Deep Learning on Amazon Web Services Read More »
Other than our last hand writing challenge, there is another Kaggle challenge featuring image recognition — TGS Salt Identification Challenge. But this time, we are going for an “upgrade”. As we are dealing with object detection. It is all about salt. In this challenge, our mission is finding geophysical images that contain salt. Oh wait, does it sound weird? Actually
U-Net and IoU for Object Detection in Image Processing Read More »
On last post, we tried our image recognition project with handwritten digits. We used a Convolutional Neural Network (CNN) to train our machine and it did pretty well with 99.47% accuracy. We learnt how a CNN works by actually implementing a model. Today, we move one step further to learn more about the CNN, let’s visualize
Visualize a Convolutional Neural Network Read More »
On our data science journey, we have solved classification and regression problems. What’s next? There is one popular machine learning territory we have not set feet on yet — the image recognition. But now the wait is over, in this post we are going to teach our machine to recognize images by using Convolutional Neural
Python Image Recognizer with Convolutional Neural Network Read More »
