Types of Neural Networks ANN CNN RNN Activation Functions Gradient Descent Explained скачать в хорошем качестве

Types of Neural Networks ANN CNN RNN Activation Functions Gradient Descent Explained

Types of Neural Networks ANN CNN RNN Activation Functions Gradient Descent Explained (High-CTR Alternative) Neural Network Mathematics Explained ANN CNN RNN Gradient Descent and Backpropagation In this video, we deeply understand how Neural Networks work mathematically and conceptually — from basic neurons to advanced architectures like CNN and RNN. Here is the GitHub repo link: https://github.com/switch2ai You can download all the code, scripts, and documents from the above GitHub repository. This session covers: • Types of Neural Networks (ANN, CNN, RNN) • How a single neuron works mathematically • Weighted summation and bias • Activation functions and why they are important • Types of activation functions (Sigmoid, ReLU, Tanh) • Hidden layers and forward propagation • Linear Regression connection with neural networks • What is Gradient Descent? • Learning rate and its importance • Vanishing Gradient problem • Backpropagation intuition • Chain rule in neural networks • Derivatives and partial derivatives explained clearly You will understand: • Why activation functions introduce non-linearity • How gradients update weights • Why learning rate matters • How errors are minimized • How deep networks learn step by step This video is perfect for: • Deep Learning beginners • AI interview preparation • Machine Learning students • Data Science learners • Anyone who wants to understand neural network mathematics clearly By the end of this video, you will have a strong conceptual and mathematical foundation in Neural Networks. Channel Name: Switch 2 AI #NeuralNetwork #DeepLearning #ANN #CNN #RNN #GradientDescent #Backpropagation #ActivationFunction #MachineLearning #ArtificialIntelligence #Switch2AI types of neural networks ANN CNN RNN explained how single neuron works activation functions explained sigmoid vs relu gradient descent tutorial learning rate explained vanishing gradient problem backpropagation explained chain rule neural network derivatives in deep learning partial derivatives machine learning linear regression neural network deep learning mathematics AI interview neural networks Switch 2 AI types of neural networks,ANN CNN RNN explained,how single neuron works,activation functions explained,sigmoid vs relu,gradient descent tutorial,learning rate explained,vanishing gradient problem,backpropagation explained,chain rule neural network,derivatives in deep learning,partial derivatives machine learning,linear regression neural network,deep learning mathematics,AI interview neural networks,Switch 2 AI,hidden layer neural network