Deep Learning

This book "Deep Learning: DL1943 Cheatsheet: DL/AI/ML Research, Engineering, Optimization & System Design" is the only book you need to master Deep Learning (DL) concepts. The focus is on practical research and engineering ideas.

This is a cheatsheet just like distilled datasets.
You do not need to read 1000s of pages. Just 150 pages is enough to revise the cutting-edge Deep Learning engineering and research ideas.

This book include:

• Chapters covering all core concepts in DL research, engineering and optimization including:
  • Basic concepts like Perceptron, Gradient Descent
  • All basic terms like epoch, topK and basic ops like MaxPool
  • Concepts to run algorithms in parallel in GPU and CPU
  • Core techniques like INT8 Quantization
  • Deep Learning System Design (with examples)
  • Numerical Analysis concepts like INT32 IEEE754, emulating FP64 using FP32.
  • Model architectures from MLP to CNN to LLM.
  • Optimization techniques across:
    • Assembly instructions like AVX512 VNNI
    • Algorithmic optimizations for DL operations like MatMul
    • Graph level operations
  • and much more.
• Each chapter is a CHEATSHEET. It includes to-the-point explanation and relevant code snippets.
• Each concept can be covered quickly in at most 4 minutes.
• Over 350 DL/AI concepts have been covered.

• Master Deep Learning/ Artificial Intelligence.
• Clear interviews for full-time positions at high-tech companies. Good enough for:
  • Software Engineer 2/3, Machine Learning or Senior Software Engineer, AI/ML GenAI or ML Analyst at Google (L3/L4/L5)
  • Software Engineer, Machine Learning (E4/E5/E6) at Meta
  • Senior Deep Learning Systems Software Engineer - AI or AI Developer Technology Engineer at NVIDIA
  • Kernel Software Engineer - AI/ML GPU or Senior Machine Learning Software Engineer at AMD
  • Lead Engineer, Senior-C/C++, machine learning at Qualcomm
  • Machine Learning Engineer at Microsoft (Level 60 to 66)
  • And much more.

• Students and developers preparing for Coding Interviews specifically for Machine Learning/Deep Learning/GenAI positions.
• Experienced developers who wanted to revise their Deep Learning skills.
• Students who need a coding sheet to revise DL/AI/ML topics quickly.