Скачать или смотреть TensorFlow vs PyTorch vs Keras: Choose Your AI Framework

🚀 Unlock the power behind modern AI! This deep dive explores the foundational Open Source AI Frameworks: TensorFlow, PyTorch, and Keras. Understand their strengths, weaknesses, core concepts, and how to choose the right toolkit for your AI projects.
In this episode, you'll learn:
💡 The Power of Open Source AI: Why collaboration, transparency, and accessibility are driving rapid innovation in the field.
🔧 TensorFlow Deep Dive: Explore Google's production-ready framework, its static/eager execution, TensorBoard visualization, and ecosystem (Lite, JS).
⚙️ PyTorch Deep Dive: Understand Meta's research-favorite framework, its dynamic graphs, Pythonic feel, and growing production capabilities (TorchScript).
📈 Keras Deep Dive: Discover the user-friendly high-level API, its multi-backend support (TF, PyTorch, JAX), and ease of use for rapid prototyping.
🤔 Choosing Your Framework: Key decision points including learning curve, flexibility, deployment, debugging, performance, and community support.
We break down the step-by-step process of:
Getting started: Installation (pip install) and core concepts (Tensors, Computational Graphs).
Comparing framework philosophies: Static vs. Dynamic graphs.
Highlighting best practices and common pitfalls for each framework.
Compare TensorFlow's production focus vs. PyTorch's research agility vs. Keras's simplicity and versatility. Understand the importance of interoperability within the Python data science ecosystem.

Gain special insights into:
🔥 The significance of Keras 3's multi-backend capability.
🌐 The broader ecosystem: Hugging Face, Scikit-learn, and emerging players like DeepSeek & Aurora m.
✅ Practical tips for getting started and avoiding common mistakes.
Subscribe for more deep dives into AI development! 👍 Like this video if you learned about AI frameworks, and comment below: Which framework (TensorFlow, PyTorch, Keras) do you prefer and why?

TIMESTAMPS:
00:00 Introduction to Open Source AI Frameworks
00:40 The Importance of Open Source in AI
02:07 Deep Dive into TensorFlow
04:19 Exploring PyTorch
05:50 Understanding Keras
07:18 Choosing the Right Framework
09:16 Beyond the Big Three: Other Open Source Tools
10:14 Getting Started with AI Frameworks
11:36 Advanced Features and Best Practices
14:05 Conclusion and Final Thoughts

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