How to become an AI tutor

 AI Tutor

generating consistently high-quality and accurately labeled data through various methods to facilitate the training of NLP algorithms. 

Natural Language Processing with Deep Learning

gain the skills to move from word representation and syntactic processing to

designing and implementing complex deep learning models for 

question answering, machine translation, and other language understanding tasks

• Design, implement, and understand your NLP neural network models, using the Pytorch framework.
• Deep learning with pytorch tutorial
  

Build the model tutorial

create datasets for model training, benchmarking, and overall advancemen

• Represent word meaning with word vectors, such as Word2Vec, SVD and GloVe.

SVD and other algorithms

• Identify semantic relationships between words in a sentence with dependency parsing.

Dependency parsing

• Make large scale word predictions with language models, recurrent neural networks (RNNs), and neural machine translation.
• Improve your NLP models and pretrain your transformers for more efficient natural language processing and understanding.

Speech and Language Processing

 divide by 3 counter

schmitt-trigger-vs-simple-inverter

Multi-scale system design

Multi-scale system design 

Complexity Theory in Axiomatic Design

Four different types of complexity are identified in axiomatic design complexity theory: 

time-independent real complexity, 

time- independent imaginary complexity, 

time-dependent combinatorial complexity and 

time-dependent periodic complexity.

AI/ML Hardware engineer

 Neural Engine HW Architect

machine learning algorithms, Transformer, convolutional neural networks and their applications in Generative AI, NLP, computer vision and image/video processing

nalyzing ML workloads on different HW architecture — profiling and identifying the performance bottleneck in the system, coming up with suggestions for performance improvement either at algorithm, SW and HW level.

• Understanding the system implications of aforementioned algorithms in terms of performance and power on a given HW architecture.
• Knowledge in LLM pipeline, image processing, camera pipeline, computational photography and natural language processing
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Fabric/Interconnect Engineer

Interconnect and Wire Engineering

Design Margin, Reliability and Scaling

Principles of VLSI design

DFT course

 dft course

Textbook 

 Essentials of Electronic Testing for Digital, Memory and Mixed-Signal VLSI Circuits by M. L. Bushnell and V.D. Agrawal, Kluwer Academic Press, Boston 2000 

Lectures

Recommended 

 System On Chip Test Architectures: Nanometer Design for Testability by L.T. Wang, C.E. Stroud, N. A. Touba, Elsevier, Morgan Kaufmann Publishers, 2009. 

 Digital System Testing and Testable Design by M. Abramovici, M. A. Breuer, and A.D. Friedman, IEEE Press, New York, 1990, 652 pages

Digital Systems testing

Design for AT-Speed Test, Diagnosis and Measurement

Design for at speed