Description

“Grokking Deep Learning

• Intuitive, Visual Explanations:

  • Learn deep learning concepts through diagrams, analogies, and plain language.

  • Breaks down topics like neural networks, gradient descent, and backpropagation into simple steps.

• Build-From-Scratch Approach:

  • Learn how deep learning works by coding every piece from the ground up—no frameworks at first.

  • Gradually introduces TensorFlow and PyTorch after the foundational understanding is built.

• No Prior AI Experience Needed:

  • Starts with the basics of math and programming required for deep learning.

  • Ideal for software developers and self-learners with minimal AI or ML background.

• Progressive Learning Path:

  • Chapters build naturally from a single neuron to full deep learning models.

  • Later chapters explore CNNs, RNNs, and practical tips for training.

• Hands-On Coding Exercises:

  • Each chapter includes small projects and Python code examples.

  • Exercises encourage experimentation and reinforcement.

• Math Made Approachable:

  • Covers core concepts like linear algebra, matrix multiplication, and derivatives in context.

  • No heavy theory—just enough math to grok how things work.

• Ethical AI and Real-World Use Cases:

  • Brief, thoughtful insights into responsible AI development and real-world applications.

• Bonus Material:

  • Cheat sheets, glossary, and downloadable Jupyter notebooks included.