Everything about Convolutional Neural Networks

Understand what deep learning is, how it differs from traditional machine learning, and why Geoffrey Hinton is called the godfather of deep learning; explore neural networks and data requirements.

Explore how a neuron uses inputs x1, x2, x3, weights, and weighted sum to produce output in a neural network with input, zero hidden, and output layers, and activation functions.

Examine activation functions that decide neuron firing by a weighted input plus bias, with examples like step, linear, and rectifier functions and their infinite output range.

Explore the step function as a threshold-based activation that activates a neuron when an input exceeds a threshold, making it suitable for binary classification but not for multiclass problems.

Analyze the linear function as an activation, explain its gradient, and why multiple linear activations collapse to a single linear mapping, so we rarely use it in hidden layers.

Explain the sigmoid activation function, its 0 to 1 range, and its use in deep neural networks' output layers. Note gradient behavior and vanishing gradient challenges.

Explore the tanh activation function, its relation to the sigmoid, and how tanh maps inputs from minus one to plus one with nonlinearity, highlighting similarities to the sigmoid.

Explore the rectified linear unit (relu) activation function and how it reduces vanishing gradients and enables sparse activations in deep neural networks, including leaky relu.

Learn how forward pass yields predictions and backward propagation updates weights to minimize the cost function, guided by batch size, learning rate, and epoch-driven iterations, with a churn prediction example.

Explore the gradient descent concept and its stochastic variant, explain how forward and backward passes update weights across batches and epochs to minimize the cost function in neural networks.

Learn what cnn stands for and how convolutional neural networks process images, voices, and videos using 1d, 2d, and 3d variants with convolution, pooling, flattening, and fully connected steps.

Explore cnn architecture: convolution, relu, pooling, flattening, and fully connected layers using feature detectors and strides to transform 7x7 images.

Explore how convolution acts as the core dimensionality reduction step in CNNs, then see how pooling, flattening, and fully connected layers transform features into a final classifier.

Explore image augmentation to fix imbalanced datasets in CNN models, using upsampling and downsampling, and techniques like flipping, rotation, noise, blur, and resizing to expand training data.

Learn how gradient descent drives neural network training by iterating over batches, epochs, and iterations, updating weights through forward and backward passes to reduce the cost.

Shows how to implement a 28x28 grayscale fashion cnn using a Keras sequential model with convolution, max pooling, dropout, flattening, dense layers, and categorical cross-entropy training.

Explore how the model summary enumerates layers and trainable parameters, detailing convolution with three cross three filters, pooling, flattening to 800, and dense layers 128, 64, 10.

Explore a healthcare case study on x-ray image classification with a convolutional neural network distinguishing normal from pneumonia, covering grayscale 200x200 resizing, a Keras sequential CNN, and binary sigmoid output.