Welcome to the Deep Learning resource section! Here, you'll find curated materials to help you dive into the world of artificial intelligence and neural networks. Whether you're a beginner or an expert, these resources will guide your journey.
📘 Essential Reading Materials
Books:
- Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (foundational theory)
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron (practical applications)
- Explore more AI books ⬆️
Online Courses:
- Andrew Ng's Deep Learning Specialization on Coursera
- Deep Learning AI by Facebook (free tutorials)
- Watch video lectures ⬆️
🛠️ Tools & Frameworks
Popular libraries:
- TensorFlow 📚
- PyTorch 🔥
- Keras 📈
- Try interactive coding ⬆️
Useful tools:
- Jupyter Notebook 📊
- Google Colab 🌐
- Docker 🐳
- Learn about AI environments ⬆️
🧭 Learning Path Suggestions
- Start with math foundations ⬆️ (linear algebra, calculus, probability)
- Practice with coding exercises ⬆️
- Build projects using real-world datasets ⬆️
- Join AI communities ⬆️ for discussions
🧑💻 Community & Forums
- Reddit's r/MachineLearning community
- Stack Overflow for technical questions
- GitHub repositories ⬆️ (open-source projects)
For advanced topics like GANs or reinforcement learning, check out our specialized guides! 🚀