Join us on The Before AGI Podcast as we explore the unsung heroes of modern AI: advanced activation functions. Go beyond ReLU and discover how functions like Swish, Mish, GELU, and SELU solve critical problems like vanishing gradients and dying neurons, enabling the deep, complex neural networks that power today's breakthroughs.
In this episode, you'll gain insights into:
💡 Why They're Essential: Understand the role of non-linearity and the limitations of older activation functions.
🧠 The Advanced Toolkit: An intuitive breakdown of Swish (self-gated), Mish (ultra-smooth), GELU (probabilistic gating for Transformers like GPT/BERT), and SELU (self-normalizing).
⚙️ Choosing the Right Function: A practical guide to selecting an activation function based on your model architecture, data characteristics, and computational needs.
⚛️ The Future of Activations: Explore the trend towards adaptive, trainable functions and hardware co-design for quantum and neuromorphic computing.
This deep dive demystifies a core, often-overlooked component of AI, revealing how these sophisticated mathematical "neurons" are crucial for making AI models learn more effectively, stably, and robustly.
Follow Before AGI Podcast for more essential explorations into core AI concepts!
TOOLS MENTIONED:
Swish / SiLU
Mish
GELU
SELU
ELiSH
Softplus
Maxout
ReLU
Leaky ReLU
PReLU
Linear / Sigmoid / Tanh
Batch Normalization
Alpha Dropout
CONTACT INFORMATION:
🌐 Website: ianochiengai.substack.com
📺 YouTube: Ian Ochieng AI
🐦 Twitter: @IanOchiengAI
📸 Instagram: @IanOchiengAI
