Transformers

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• The LLM Triangle Principles

• A Beginner's Guide to Quantization in Deep Learning with PyTorch

🧠 Transformers

Transformers are neural network architectures that excel at sequence transduction tasks like translation, speech recognition, and text-to-speech. They were pivotal in OpenAI's language models and DeepMind's AlphaStar, a top-performing Starcraft AI.

Recurrent Neural Networks (RNNs) and Long-Short Term Memory (LSTM)

RNNs handle sequences by passing information through loops, allowing them to process text word by word. However, they struggle with long-term dependencies as the information deteriorates over time. LSTMs improve by selectively remembering important information through cell states, but they still face limitations with long sequences and parallel processing.

Attention Mechanisms

Attention mechanisms help by allowing models to focus on relevant parts of the input sequence. This improves translation accuracy by considering dependencies between words. Despite their advantages, attention mechanisms in RNNs still suffer from limited parallelization.

Convolutional Neural Networks (CNNs)

CNNs can parallelize operations, reducing the "distance" between input and output words. However, they don't fully address the problem of dependencies in sequences.

Transformers

Transformers utilize self-attention and multihead attention mechanisms to handle dependencies efficiently while enabling parallel processing. They consist of encoders and decoders, with each encoder processing inputs through self-attention and feed-forward layers. Multihead attention allows the model to focus on different aspects of the input, improving translation quality.

Self-Attention and Multihead Attention

Self-attention calculates the importance of each word relative to others using query, key, and value vectors. This is done in parallel for faster processing. Multihead attention further refines this by considering multiple types of dependencies.

Positional Encoding

Transformers also incorporate positional encoding to maintain word order information, crucial for accurate sequence transduction.

In summary, transformers revolutionized sequence transduction by combining the strengths of attention mechanisms and parallel processing, overcoming the limitations of RNNs and CNNs.

Top 10 AI Repositories to Watch in 2024

1. Fastai

   • Description: Simplifies training neural networks with state-of-the-art models.

   • GitHub: Fastai GitHub

2. Hugging Face Transformers

   • Description: Comprehensive library for NLP with pre-trained models.

   • GitHub: Transformers GitHub

3. OpenCV AI Kit (OAK)

   • Description: Open-source ecosystem for computer vision tasks, optimized for edge devices.

   • GitHub: OAK GitHub

4. DeepSpeech

   • Description: Open-source speech-to-text engine developed by Mozilla.

   • GitHub: DeepSpeech GitHub

5. Jina

   • Description: Open-source neural search framework for various data forms.

   • GitHub: Jina GitHub

6. Allennlp

   • Description: High-level library for NLP research, maintained by the Allen Institute for AI.

   • GitHub: Allennlp GitHub

7. Detectron2

   • Description: Next-generation library for object detection and segmentation by Facebook AI Research.

   • GitHub: Detectron2 GitHub

8. Haystack

   • Description: Open-source framework for building end-to-end NLP pipelines.

   • GitHub: Haystack GitHub

9. Catalyst

   • Description: Accelerated deep learning framework focusing on reproducibility and rapid experimentation.

   • GitHub: Catalyst GitHub

10. MindSpore

    • Description: Deep learning framework by Huawei, optimized for the Ascend AI processor.

    • Website: MindSpore