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Understanding Perceptron

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Understanding Perceptron

Deep Learning Understanding Perceptron

Understanding Perceptron

In this class, We discuss Understanding Perceptron.

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Understanding Nervous System for Neural Networks

Understanding Perceptron

Training a Single Perceptron Back Propagation

Multi-layer Perceptron Neural Network

Training Multi-layer Perceptron Back Propagation

Sigmoid and Tanh Activation Functions

The Power of Neural Network How it Provide Nonlinearity

Relu Leaky Relu and Swish Activation Functions

Convex Non-Convex Functions and Contour for Neural Network

Saddle Point in Neural Network

Problem with SGD in Neural Network

Exponential Weighted Average

SGD with Momentum Optimizer

Ada Grad and Ada Delta Optimizer

Adam Optimizer for Neural Network

How Normalization Helps Gradient Descent

Scaling and Shifting For Batch Normalization

Need of Batch Normalization

How Batch Normalization Implemented

Regularization in Neural Network

Dropout in Neural Network

Softmax Activation Function

Weather Dataset for Implementing Artificial Neural Network

Artificial Neural Network Code on Weather Dataset ANN

Images and Neural Network

Convolution Operation on Images Neural Network

Need of Padding in Convolution Layer

Edge Detection in Convolution Layer

Stride and Image Size After Convolution

Convolution on Color Images

Max pooling in Convolution Neural Network CNN

How Translation Invariance is solved using Convolution Neural Network CNN

Disadvantage of Max pooling in CNN

Data Augmentation Uses in Neural Network

Flattening and Simple Convolution Neural Network

Return to Deep Learning

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