AI By Doing

动手实战人工智能 AI By Doing

This comprehensive tutorial provides a practical, hands-on approach to learning AI, machine learning, and deep learning. It focuses on bridging the gap between theory and practice, offering code examples and explanations.

Key Features:

• Practical Implementation: Combines theoretical explanations with Python code examples.
• Broad Coverage: Covers supervised learning, unsupervised learning, deep learning, and reinforcement learning.
• Frameworks: Includes TensorFlow and PyTorch.
• Deployment: Guides on model deployment and inference.
• Mathematical Foundations: Includes a review of essential mathematical concepts.
• Tools: Covers essential tools like Jupyter Notebook, NumPy, Pandas, and Matplotlib.

Use Cases:

• Learning machine learning and deep learning algorithms.
• Implementing AI models using Python.
• Deploying machine learning models.
• Understanding the mathematical foundations of AI.
• Using popular AI frameworks like TensorFlow and PyTorch.