Deep Learning 1-Convolution Neural Network

Learn A-Z Deep Learning in 15 Days

Deep Learning

Deep Learning is a subfield of machine learning concerned that teaches computers to do what comes naturally to humans. The term “deep” usually refers to the number of hidden layers in the neural network. Deep learning models are trained by using large sets of labeled data and neural network architectures that learn features directly from the data without the need for manual feature extraction. One of the most popular types of deep neural networks is known as convolutional neural networks.

Convolutional Neural Network

CNNs, like neural networks, are made up of neurons with learnable weights and biases. Each neuron receives several inputs, takes a weighted sum over them, passes it through an activation function, and responds with an output.CNNs have wide applications in image and video recognition, recommender systems, and natural language processing.

Convolutional Neutral Network is made of Feature extraction layer  & Classification layer.

Feature Extraction

The main objective of convolution is to extract features such as edges, colors, corners from the input.

Layer performs a dot product between two matrices, where one matrix(known as filter/kernel)is the set of learnable parameters, and the other matrix is the restricted portion of the image.

Fig:-Maths Behind Convolution

Gif:- Input image & Filter Convolution 

ReLU(Rectified Linear Unit)

ReLU replaces all negative pixel values in the feature map by zero. The purpose of ReLU is to introduce non-linearity in our ConvNet.

Pooling Layer

The dimensionality of the feature map gets reduced keeping the important information. sometimes this spatial pooling is also called Downsampling or subsampling.

Fully Connected Layer

The Fully Connected layer is a traditional Multi-Layer Perceptron that uses a softmax activation function in the output layer. Fully Connected layer is to use these features for classifying the input image.

**Imp Rules

- Overfitting -val_Acc +val_Loss, +drop -network 

- Underfitting +val_Acc -val_loss, -drop +network +dataset  

Summary

In the next blog, we will start  Deep Learning Classification with Custom.
https://sngurukuls247.blogspot.com/2020/05/deep-learning-2-classification-with.html

                                                                                                                                                      

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