transformers
Understanding the attention mechanism is essential for grasping how modern AI generates video, text, and synthetic media. This technical guide breaks down the architecture that powers everything from GPT to deepfake generators.
neural networks
Dropout is a powerful regularization technique that prevents overfitting by randomly deactivating neurons during training. This counterintuitive approach forces networks to learn robust, distributed representations that generalize better to unseen data.
neural networks
Liquid Neural Networks introduce adaptive, continuous-time dynamics that outperform traditional RNNs with fewer parameters. The architecture offers real-time processing capabilities crucial for video analysis and autonomous systems through dynamic state adjustments.
CNNs
Deep dive into convolutional neural networks: the foundational architecture powering deepfake detection, video generation, and synthetic media analysis through convolution, pooling, and feature extraction.
neural networks
Deep dive into the mathematical foundations of neural network training: how loss functions measure model performance and optimization algorithms like SGD, Adam, and RMSprop guide learning through gradient descent.
LLM Architecture
Deep dive into the technical progression of large language model architectures, from the foundational Transformer through Mixture of Experts to cutting-edge Mixture of Routers, examining how each innovation addresses scaling and efficiency challenges.
neural networks
Deep dive into batch normalization, the technique that revolutionized neural network training by solving internal covariate shift. Essential knowledge for building stable AI models including video generators and deepfake systems.
AI Agents
Technical deep dive into designing agentic AI systems with human-like memory decay, self-evaluation capabilities, and persistent personalization. Explores implementation strategies for creating adaptive AI agents that learn and forget contextually.
Deep Learning
Deep dive into the technical foundations of training neural networks, covering backpropagation, gradient descent, optimization algorithms, and the mathematical principles that power modern AI systems from video generation to deepfakes.
neural networks
Deep dive into activation functions, the mathematical components that enable neural networks to learn complex patterns. Explores ReLU, sigmoid, tanh, and modern variants with technical implementation details.
Symbolic AI
Exploring the technical foundations of symbolic AI and how it differs from neural networks. This deep dive examines knowledge representation, logical reasoning, and hybrid architectures that combine symbolic and subsymbolic approaches.
AI Agents
New research proposes integrating neural networks, symbolic reasoning, and causal inference into a unified architecture for AI agents that can handle complex multi-objective tasks with improved robustness beyond traditional prompt engineering.