**Visualizing Neural Network Sizes****Visualization of an example Dogs vs Cats neural network classifier****Visualization of the forward pass calculation and path for a neural network****Single neuron with 3 inputs example****A single neuron with 4 inputs****3 neuron layer with 4 inputs****Arrays and their shapes****Dot Product in Python****Using the dot product for a neuron's calculation****Using the dot product with a layer of neurons****Example of what an array of a batch of samples looks like, compared to a single sample.****How batches can help with fitment****How a matrix product is calculated****Matrix product with row and column vectors****How a tranpose/transposition works****Matrix product with row and column vectors with a batch of inputs to the neural network****Adding biases after the matrix product from a batch of inputs****Why & how two or more hidden layers w/ nonlinear activation functions works with neural networks/deep learning****Example of a linear function****Example of a parabolic function****Parabolic function derivatives****Parabolic Function****Parabolic Function 2 Derivatives Graph****Live SGD Optimization for neural network with Learning Rate of 1.0. Epilepsy Warning, there are quick flashing colors.****Live SGD Optimization for neural network with 0.5 Learning Rate. Epilepsy Warning, there are quick flashing colors.****Live SGD Optimization for neural network with a Decaying (1e-2) Learning Rate . Epilepsy Warning, there are quick flashing colors.****Live SGD Optimization for neural network with a slower Decaying (1e-3) Learning Rate. Epilepsy Warning, there are quick flashing colors.****Live SGD Optimization for neural network with a 1e-3 Decaying Learning Rate from 1.0, along with momentum (0.5). Epilepsy Warning, there are quick flashing colors.****Live SGD Optimization for neural network with a 1e-3 Decaying Learning Rate from 1.0, along with momentum (0.9). Epilepsy Warning, there are quick flashing colors.****AdaGrad Optimizer Example****RMSProp Optimizer for Neural Networks****RMSProp Optimizer with: LR: 0.2, decay: 1e-5, rho: 0.999****Adam Optimizer for Neural Networks with 0.02 learning rate and 1e-5 decay****Adam Optimizer for Neural Networks with 0.05 learning rate and 5e-7 decay****How weights and biases impact a single neuron****Step Function Animation****The math behind an example forward pass through a neural network****How a transpose works****Why we need to transpose weights****Regression Demo with rectified linear (ReLU) activation function****Analytical Derivative****Y Intercept****Analytical Derivative X****Analytical Derivative 2x****Analytical Derivative 3x^2****AnalyticalDerivative 3x^2 + 2x****Analytical Derivative 5x^5+4x^3-5****AnalyticalDerivative x^3+2x^2-5x+7****Backpropagation Example****Simplifying Neuron Derivative****Learning Rate Local Minimum****Another local minimum example****Learning Rate Small Local Minimum****Learning Rate Too Small, 200 Steps****Learning Rate too small 100 Steps****Learning Rate too small 50 Steps****Learning Rate Too Big****Learning rate Way Too Big****Gradient Explosion****Good Enough Learning Rate****Good Learning Rate****Testing Data intuition****Cross validation****Regularization 1. Epilepsy Warning: quick flashing colors****Regularization 2. Epilepsy Warning: quick flashing colors****Regularization 3. Epilepsy Warning: quick flashing colors****Dropout visualized****Dropout training example 1 Epilepsy Warning: quick flashing colors****Dropout training example 2 Epilepsy Warning: quick flashing colors****Regression Example 1****Regression Example 2****Regression Example 3****Regression Example 4****Regression Example 5****Regression Example 6****Regression Example 7****Regression Example 8****Regression Example 9**