Welcome back to the second part of our machine learning project initiation series! In the first article, we had a quick refresher of CRISP-DM, and I promised we’d start with the first phase – but hold on, let’s backtrack a bit. Setting the Stage for Success Before we jump into the first phase, let’s talkContinue reading “Project Guide – Environment – Part 2”
Tag Archives: Deep Learning
ML Zoomcamp 2023 – Deep Learning – Part 14
Using the model In the last section we trained the final model – model on bigger images and saved the best one which we want to use now to test it on test dataset and make predictions. In this article we will cover: Loading the model Evaluating the model The evaluation is done quite easilyContinue reading “ML Zoomcamp 2023 – Deep Learning – Part 14”
ML Zoomcamp 2023 – Deep Learning – Part 13
Training a larger model In the sections before we use images of size 150×150 because the model can be trained faster (here:4 times faster). This is a good way when experimenting with parameters. Now we want to train on bigger images – let’s use images of size 299×299. Therefor we reuse the previous code forContinue reading “ML Zoomcamp 2023 – Deep Learning – Part 13”
ML Zoomcamp 2023 – Deep Learning – Part 12
Data Augmentation In the last section we talked about how to stabilize the network performance while we use dropout. This way we focus on overall shape instead of focussing on details like logos. It works because in each epoch another part of the network (not of the image) is hided/frozen. So small details like logosContinue reading “ML Zoomcamp 2023 – Deep Learning – Part 12”
ML Zoomcamp 2023 – Deep Learning – Part 11
Regularization and Dropout When training a model for 10 epochs, then the model will see the same image 10 times. If there is a special sign like a logo, the model recognize it and tend to declare everything as t-shirt that has that logo. That means the model could make many mistakes in classification inContinue reading “ML Zoomcamp 2023 – Deep Learning – Part 11”
ML Zoomcamp 2023 – Deep Learning – Part 10
Adding more layers It’s possible to add more inner layers after the vector representation. Previously we had one inner layer before outputting the prediction. This layer does some intermediate processing of the vector representation. These inner layers make the neural network more powerful. Adding one inner dense layer Usually adding one or two additional layersContinue reading “ML Zoomcamp 2023 – Deep Learning – Part 10”
ML Zoomcamp 2023 – Deep Learning – Part 8
Adjusting the learning rate What’s the learning rate ? Let’s use an analogy. Imagine that learning rate is how fast you can read and you read one book per quarter. That means you can read 4 books per year. Somebody else can read 1 book per day, so he can read many books per year.Continue reading “ML Zoomcamp 2023 – Deep Learning – Part 8”
ML Zoomcamp 2023 – Deep Learning – Part 7
This is the second part of Transfer Learning section. While in the last article we looked at reading image data this article covers the training part. Transfer Learning – Part 2/2 Train Xception on smaller images (150×150) (Better to run it with a GPU) So far for reading the data, now let’s train a model.Continue reading “ML Zoomcamp 2023 – Deep Learning – Part 7”
ML Zoomcamp 2023 – Deep Learning – Part 6
This part is devided into 2 sections. This article is about the first part where we want to read image data using the ImageDataGenerator. The second article covers the second part where we want to train an Xception model. Transfer Learning – Part 1/2 This time we want to use an already trained network thatContinue reading “ML Zoomcamp 2023 – Deep Learning – Part 6”
ML Zoomcamp 2023 – Deep Learning – Part 4
In this part we’ll use a pre-trained convolutional neural network to understand what is on the image we load previously. Pre-trained convolutional neural networks This time we want to take an image and an off-the-shelf neural network that was already trained by somebody, so we can use it. Now we want to use a specialContinue reading “ML Zoomcamp 2023 – Deep Learning – Part 4”
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