Lenet - 5 Model in Deep Learning in Introduction Part

Lenet - 5 Model in Deep Learning in Introduction Part which is used for identificatin of Handwritten with entire convolutional process

1. Module 1 Introducing Deep Learning LeNet - 5

2. LeNet – 5 - History Gradient-Based Learning Applied to Document Recognition Y.LeCun , L Bottou , Y.Bengio an dP.Haffner Proceedings of the IEEE, 86(11):2278 – 2324, November 1998

3. LeNet – 5 – History of Representative CNN Models

4. LeNet – 5 - Architecture

5. LeNet – 5 – Architecture steps

6. CNN General Formula Training phase = learn weights from many images. Prediction phase = use those learned weights to classify new input images.

7. LeNet-5 Architecture two main parts Let’s break it into two phases : 1. Training Phase You give many labeled images (say handwritten digits 0–9). LeNet-5 does forward pass ( conv → tanh → pooling → fully connected → output). The output is compared with the ground truth label (like "this image is digit 5"). Error (loss) is calculated. Backpropagation adjusts the weights and biases so that next time the prediction is closer to the truth. After repeating this on thousands of images , the model learns good weights.

8. LeNet-5 Architecture two main parts 2. Prediction (Inference) Phase Now training is finished, weights & biases are fixed . When you give a new input image (say a handwritten "3"), the network runs forward only: input →  conv  →  tanh  → pooling → fully connected → output scores At the last layer (10 neurons), the highest score indicates the digit .

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16. THANK YOU