Convolutional Neural Networks || Part-02 || code
Convolutional Neural Networks || Part-02 || Code
In this session, I delve into Convolutional Neural Networks (CNNs) and their applications for both tabular and image datasets. Specifically, I explore how to use CNNs with the CIFAR-10 and Fashion MNIST datasets from Keras.
Key Concepts Covered:
Checkpoints: These allow you to save model weights during training, enabling you to resume training from where you left off.
Optimizer: The optimizer determines how the model's weights are updated during training. Common optimizers include Adam, SGD, and RMSprop.
Epochs: An epoch represents one complete pass through the entire training dataset.
-Batch Size: The number of samples used in each iteration during training.
Components of a CNN:
1. Convolution Layer: Applies multiple filters to the input, creating feature maps that capture relevant patterns.
2. Pooling Layer: Reduces the spatial dimensions of the feature maps, aiding in translation invariance.
3. ReLU (Rectified Linear Unit): Introduces non-linearity, allowing the network to learn complex patterns.
Remember that all images used in this session were collected from the internet.
Part-01 : [Watch here]( • Convolutional Neural Networks || Part-01 )
code: https://github.com/sajjadrahman56/Lea...
Connect with me:
GitHub: [github. com/sajjadrahman56](https://github.com/sajjadrahman56)
Twitter: [@sajjadrahman56]( / sajjadrahman56 )
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