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

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

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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

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Switch 2 AI


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