NEURAL NETWORKS AND ITS COMPONENTS| DEEP LEARNING AND ITS APPLICATION | SNS INSTITUTIONS скачать в хорошем качестве

NEURAL NETWORKS AND ITS COMPONENTS| DEEP LEARNING AND ITS APPLICATION | SNS INSTITUTIONS

At the heart of Deep Learning lies a powerful idea inspired by the human brain—Neural Networks. A neural network is made up of layers of interconnected units called neurons, each working together to learn patterns from data. Instead of being explicitly programmed, these networks learn from experience, improving their performance with every iteration. 🔹 Key Components of a Neural Network: • Input Layer – Receives raw data (images, text, numbers) and passes it into the network. • Weights – Determine the importance of each input; learning happens by adjusting these values. • Bias – Helps the model fit the data better by shifting the activation function. • Hidden Layers – Where the real learning happens; multiple layers enable deep understanding of complex patterns. • Activation Functions – Introduce non-linearity, allowing the network to learn complex relationships (ReLU, Sigmoid, Tanh). • Output Layer – Produces the final prediction or classification result. • Loss Function – Measures how far predictions are from actual values. • Optimizer – Updates weights efficiently to minimize errors (SGD, Adam, RMSProp). #snsinstitutions #snsdesignthinkers #designthinking