Join us on The Before AGI Podcast as we unpack the essential open-source frameworks driving the AI revolution: TensorFlow, PyTorch, and Keras. Discover the core philosophies, strengths, weaknesses, and ideal use cases for these foundational toolkits.
In this episode, you'll gain insights into:
💡 Open Source Power: Why the open-source model is fundamental to AI progress.
🔧 Framework Deep Dives: Understanding TensorFlow's production focus, PyTorch's research agility, and Keras's user-friendly approach.
⚙️ Key Features & Concepts: Exploring Tensors, computational graphs (static vs. dynamic), visualization (TensorBoard), deployment tools (TorchScript), and Keras 3's multi-backend flexibility.
🤔 Making the Choice: Key factors like learning curve, flexibility, deployment needs, debugging ease, and community support.
🌐 Beyond the Big Three: Recognizing the rich ecosystem including Hugging Face, Scikit-learn, and newer players.
This deep dive provides a clear comparison, helping developers, students, and tech enthusiasts understand the tools that bring AI models to life and navigate the choices involved in starting an AI project.
Follow Before AGI Podcast for more essential explorations of AI technologies and concepts!
TOOLS MENTIONED:
TensorFlow (incl. TensorFlow Lite, TensorFlow.js, TensorBoard, TF data, TensorFlow Hub, TF module)
PyTorch (incl. TorchScript, TorchServe, torch.nn.Module)
Keras (incl. Keras 3)
JAX (Mentioned as Keras backend)
NumPy
Pandas
Matplotlib
Scikit-learn
Hugging Face (Mentioned as ecosystem player)
DeepSeek (Mentioned as ecosystem player)
Together AI (Mentioned as ecosystem player)
H2O.ai (Mentioned as ecosystem player)
Aurora m (Mentioned as ecosystem player)
CONTACT INFORMATION:
🌐 Website: ianochiengai.substack.com
📺 YouTube: Ian Ochieng AI
🐦 Twitter: @IanOchiengAI
📸 Instagram: @IanOchiengAI
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