Hand-picked selection of articles on AI fundamentals/concepts
13 days ago
- #AI Fundamentals
- #Machine Learning
- #Neural Networks
- A curated selection of articles covering AI fundamentals, from building neural networks to training and evaluating results.
- Key algorithms and architectures include Linear/Logistic Regression, k-Nearest Neighbors, SVMs, Decision Trees, GANs, Diffusion Models, and Reinforcement Learning.
- Important data and training concepts: Data Sampling, Regularization, Gradient Descent, Loss Functions, Fine-tuning, and Distributed Training.
- Vision-related topics: Vision Transformers (ViT), Receptive Fields, Residual Networks, and GPT-4o Image Generation.
- NLP fundamentals: Word Embeddings, Transformers, LLMs, RAG, Tokenization, and Machine Translation.
- Multimodal models like BERT, GPT, LLaMA, Gemini, and specialized tools such as Toolformer and Visual ChatGPT.
- Evaluation methods, MLOps, On-Device AI (Model Compression, Federated Learning), and miscellaneous topics like PyTorch vs. TensorFlow.