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Deep Learning

  • About
  • Duration

Deep learning is a subset of machine learning that involves neural networks with multiple layers (deep neural networks). It leverages hierarchical learning representations to automatically discover intricate patterns and features from data. Widely used in tasks such as image and speech recognition, natural language processing, and autonomous systems, deep learning excels at handling large, complex datasets. It requires substantial computational power and extensive training data but has achieved remarkable success in various applications, making it a key technology in artificial intelligence development.

Length : 40 Hours

Course Content

  • Introduction to Deep Learning:
  • Overview of neural networks and their evolution
    Basics of deep learning, including architecture and components
  • Mathematical Foundations:
  • Linear algebra and calculus relevant to deep learning
    Optimization techniques for model training
  • Neural Networks Basics:
  • Perceptrons, activation functions, and feedforward neural networks
    Backpropagation algorithm for training neural networks
  • Deep Neural Networks:
  • Convolutional Neural Networks (CNNs) for image recognition
    Recurrent Neural Networks (RNNs) for sequential data processing
    Transfer learning and pre-trained models
  • Optimization Techniques:
  • Stochastic Gradient Descent (SGD) and its variants
    Regularization methods to prevent overfitting

  • Advanced Architectures:
  • Generative Adversarial Networks (GANs) for image generation
    Long Short-Term Memory (LSTM) networks for sequence modeling
    Attention mechanisms in deep learning
  • Deep Learning Frameworks:
  • Hands-on experience with popular frameworks like Tensor Flow and PyTorch
    Building and training models using real-world datasets
  • Applications of Deep Learning:
  • Image and speech recognition
    Natural Language Processing (NLP)
    Autonomous vehicles and reinforcement learning
  • Ethical and Social Implications:
  • Considerations and challenges in deploying deep learning models
    Ethical considerations in AI and deep learning applications
  • Project Work:
  • Practical implementation of deep learning concepts
    Developing and presenting a deep learning project