Join us on The Before AGI Podcast as we explore how we train the ultra-deep neural networks that power modern AI. For decades, making networks deeper made them impossible to train due to the “gradient flow crisis.” Discover the ingenious breakthroughs that solved this fundamental problem.
In this episode, you’ll gain insights into:
💥 Vanishing & Exploding Gradients: An intuitive explanation of the twin crises that crippled early deep learning.
💡 The ReLU Revolution: Why this simple activation function was a game-changer, and the “dying ReLU” problem it created.
🔗 Architectural Highways: A deep dive into Skip Connections (ResNets) and how they create “gradient highways” that allow information to flow through hundreds of layers.
📊 Normalization Layers: The critical role of Batch Normalization (for CNNs) and why Layer Normalization is essential for Transformers.
⚙️ The Full Toolkit: Understanding the synergy between smart activations, weight initialization, gradient clipping, skip connections, and normalization that underpins today’s successful models.
This episode demystifies the core engineering and architectural innovations that unlocked the deep learning era, making today’s billion-parameter models possible.
Follow Before AGI Podcast for more essential explorations into core AI concepts!
TOOLS MENTIONED:
Backpropagation
Vanishing/Exploding Gradients (Concept)
Activation Functions (ReLU, GELU, Swish, etc.)
Weight Initialization (Xavier, He)
Gradient Clipping
Skip Connections / Residual Connections
ResNet (Residual Network)
DenseNet
U-Net
Batch Normalization
Layer Normalization
Transformer
CONTACT INFORMATION:
🌐 Website: ianochiengai.substack.com
📺 YouTube: Ian Ochieng AI
🐦 Twitter: @IanOchiengAI
📸 Instagram: @IanOchiengAI
