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A state-of-the-art model, open-weight, with a granular Mixture-of-Experts architecture that fuses instruct, reasoning and agentic skills. • 3 items • Updated Mar 16 • 74

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Talk to Gemini

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Talk to Gemini using Google's multimodal API

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FastRTC Custom UIs

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A collection of FastRTC demos that showcase how to built a Custom UI for your server • 4 items • Updated Apr 7, 2025 • 2

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Llasa 3b Tts

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Zero Shot voice cloning with llasa 3b (Unofficial Demo)

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Mixture of Expert Models by me. This leverages the power of multiple models at the same time during generation for next level performance. • 60 items • Updated 18 days ago • 17

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Optimizing Sub-billion Parameter Language Models for On-Device Use Cases (ICML 2024) https://arxiv.org/abs/2402.14905 • 49 items • Updated Mar 2 • 141

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It's not every day you see a research paper named "Alice's Adventures in a Differentiable Wonderland," and when you open it, it's a 281-page book!

I haven't completed it yet, but this amazing work, written by Simone Scardapane, is a fascinating introduction to deep neural networks and differentiable programming.

Some key technical highlights:

• Covers core concepts like automatic differentiation, stochastic optimization, and activation functions in depth

• Explains modern architectures like convolutional networks, transformers, and graph neural networks

• Provides mathematical foundations including linear algebra, gradients, and probability theory

• Discusses implementation details in PyTorch and JAX

• Explores advanced topics like Bayesian neural networks and neural scaling laws

The book takes a unique approach, framing neural networks as compositions of differentiable primitives rather than biological analogs. It provides both theoretical insights and practical coding examples.

I especially enjoyed the sections on:

• Vector-Jacobian products and reverse-mode autodiff
• Stochastic gradient descent and mini-batch optimization
• ReLU, GELU, and other modern activation functions
• Universal approximation capabilities of MLPs

Whether you're new to deep learning or an experienced practitioner, this book offers valuable insights into the fundamentals and latest developments. Highly recommended for anyone working with neural networks!