1. Development and Release of Llama 2: 2. Capabilities of Large Language Models (LLMs): 3. Training Methodology of LLMs: 4. Comparison with Other Models: 5. Introduction of Llama 2 and Llama 2-Chat: 6. Novel Observations: 7. Models Being Released: 8. Release Considerations: 9. Paper Structure: The rest of the paper discusses the pre-training and fine-tuning
An audio spectrogram provides an intuitive representation of the frequency spectrum of an audio signal as it changes over time. For a segment of audio data over a period of time, it can be abstracted into a finite-length audio spectrogram. An audio spectrogram has a 2D representation, which can be visualized as a flat image.
Understanding ‘Encoders’ Visual Encoders Training Neural Networks Pre-trained Encoders Common Datasets for Pre-training Types of Pre-trained Visual Encoders Fine-tuning & Transfer Learning Applications Challenges & Considerations Conclusion Pre-trained visual encoders leverage prior knowledge from extensive datasets to offer a head start when tackling new visual tasks. They encapsulate a form of “transfer learning,” allowing models
Let’s unpack the concept of a “learnable interface layer” step by step, especially in the context of AI, language models, and Transformer architectures: Basic Understanding of Neural Networks Neural networks consist of layers of interconnected nodes (or neurons). Each connection has a weight, which is adjusted during training to minimize the difference between the predicted
Basic Understanding of Modality In the context of AI, a “modality” refers to a specific type of data or way of interacting. For example, text, images, audio, and videos are different modalities. Each modality offers a unique representation of information. What is Multi-Modality? Multi-modality involves combining information from multiple modalities to improve AI’s understanding, representation,
