Deep Learning
Deep learning is a subset of machine learning that uses artificial neural networks with multiple layers to learn hierarchical representations of data. Deep learning has achieved breakthrough results in vision, language, and speech.
Understanding Deep Learning
Deep learning is a subset of machine learning that uses neural networks with multiple hidden layers to automatically learn hierarchical representations from data. By stacking layers of interconnected neurons with non-linear activation functions, deep learning models can capture complex patterns in images, text, audio, and structured data without manual feature engineering. Architectures like convolutional neural networks revolutionized computer vision, while recurrent neural networks and transformers transformed natural language processing. Training deep models requires large datasets, significant GPU compute via platforms like CUDA, and optimization through gradient descent. Deep learning powers applications ranging from face recognition and speech synthesis to autonomous driving and drug discovery. The field continues to advance with innovations such as diffusion models, foundation models, and techniques like fine-tuning and distillation.
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Deep Learning
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Activation Function
An activation function is a mathematical function applied to a neuron's output to introduce non-linearity into a neural network. Common activation functions include ReLU, sigmoid, and tanh, each with different properties for gradient flow.
Adam Optimizer
Adam (Adaptive Moment Estimation) is an optimization algorithm that combines the benefits of AdaGrad and RMSProp. It adapts learning rates for each parameter using estimates of first and second moments of gradients.
Adapter Layers
Adapter layers are small trainable modules inserted into a pre-trained model to enable parameter-efficient fine-tuning. They allow task adaptation while keeping the original model weights frozen.
Attention Mechanism
An attention mechanism allows neural networks to focus on the most relevant parts of the input when producing each element of the output. Attention is the foundational innovation behind the Transformer architecture and modern large language models.
Autoencoder
An autoencoder is a neural network trained to compress input data into a compact representation and then reconstruct it. Autoencoders are used for dimensionality reduction, denoising, and learning latent representations.
Backpropagation
Backpropagation is the algorithm used to train neural networks by computing gradients of the loss function with respect to each weight. It propagates error signals backward through the network to update weights and minimize prediction errors.
Batch Normalization
Batch normalization is a technique that normalizes layer inputs across mini-batches during training to stabilize and accelerate neural network training. It reduces internal covariate shift and allows higher learning rates.
Batch Size
Batch size is the number of training examples used in one iteration of gradient descent. Larger batches provide more stable gradient estimates but require more memory, while smaller batches add beneficial noise.