Row 6449

Row ID: 6449 | Dataset Entry | Axioma AXP Content Repository

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https://preview.redd.it/z1y2llmd3mzc1.png?width=1280&format=png&auto=webp&s=404610bf085715c10a54ddd4fa5c425579726ef2

Video 3: Enhancing Classification with Keras Tuner:

🎯 Take your monkey species classification to the next level by leveraging the power of Keras Tuner.

So , how can we decide how many layers should we define ? how many filters in each convolutional layer ?

Should we use Dropout layer ? and what should be its value ?

Which learning rate value is better ? and more similar questions.

Optimize your CNN model's hyperparameters, fine-tune its performance, and achieve even higher accuracy.

Learn the potential of hyperparameter tuning and enhance the precision of your classification results.

This is the link for part 3: [https://youtu.be/RHMLCK5UWyk](https://youtu.be/RHMLCK5UWyk)&list=UULFTiWJJhaH6BviSWKLJUM9sg

I shared the a link to the Python code in the video description.

This tutorial is part no. 3 out of 5 parts full tutorial :

🎥 Image Classification Tutorial Series: Five Parts 🐵

In these five videos, we will guide you through the entire process of classifying monkey species in images. We begin by covering data preparation, where you'll learn how to download, explore, and preprocess the image data.

Next, we delve into the fundamentals of Convolutional Neural Networks (CNN) and demonstrate how to build, train, and evaluate a CNN model for accurate classification.

In the third video, we use Keras Tuner, optimizing hyperparameters to fine-tune your CNN model's performance. Moving on, we explore the power of pretrained models in the fourth video,

specifically focusing on fine-tuning a VGG16 model for superior classification accuracy.

Lastly, in the fifth video, we dive into the fascinating world of deep neural networks and visualize the outcome of their layers, providing valuable insights into the classification process

Enjoy

Eran

#Python #Cnn #TensorFlow #Deeplearning #basicsofcnnindeeplearning #cnnmachinelearningmodel #tensorflowconvolutionalneuralnetworktutorial

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