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Artificial Intelligence in Advance Computing

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  • Artificial Intelligence in Advance Computing

 

  1. Introduction to Artificial Intelligence

  2. Programming Crash Course

    • Python: Basics, Loops, Blocks and Statements, Lists, Sets, File I/O, Modules and Functions, OOP Concepts
    • Keras: Introduction, Installation, and Configuration
    • TensorFlow: Tensors, TensorFlow Installation, TensorFlow Basics
  3. Data Analytics

    • Big Data Analytics: Expand coverage to include big data analytics concepts and technologies such as Apache Hadoop, Apache Spark, and distributed computing frameworks for processing large-scale datasets.
  4. Mathematics for AI

    • Probability Theory and Statistics: Expand coverage of probability theory and statistical methods relevant to AI, including Bayesian inference, statistical hypothesis testing, and probabilistic graphical models.
  5. Deep Learning

    • Introduction to Neural Networks
    • Deep Neural Networks
    • Perceptrons
    • RNN (Recurrent Neural Networks)
    • CNN (Convolutional Neural Networks)
    • LSTM (Long Short-Term Memory)
    • Deep Belief Network
    • Semantic Hashing
    • Building Deep Learning Models/Neural Networks using Keras and TensorFlow
    • Testing and Training the Models
  6. Convolutional Neural Networks

  7. Natural Language Processing & Computer Vision

    • Multimodal Learning: Explore methods for combining text, image, and other modalities in AI applications, such as multimodal embeddings, fusion architectures, and cross-modal retrieval.
    • Ethical Considerations in AI Applications
  8. AI Compute Platforms, Applications & Trends

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