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Deep Learning

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Topics & Syllabus 30

#1 Introduction to Deep Learning
An overview of deep learning, its applications, and how it differs from traditional m...
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#2 Neural Networks
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#3 Activation Functions
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#4 Loss Functions
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#5 Backpropagation
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#6 Convolutional Neural Networks (CNNs)
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#7 Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM)
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#8 Generative Adversarial Networks (GANs)
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#9 Transfer Learning
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#10 Ethical Considerations in Deep Learning
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#11 Introduction to Deep Learning
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#12 Neural Networks
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#13 Convolutional Neural Networks (CNNs)
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#14 Recurrent Neural Networks (RNNs)
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#15 Training Deep Learning Models
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#16 Transfer Learning
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#17 Generative Adversarial Networks (GANs)
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#18 Deep Reinforcement Learning
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#19 Ethical Considerations in Deep Learning
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#20 Future Trends in Deep Learning
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#21 Introduction to Deep Learning
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#22 Artificial Neural Networks
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#23 Convolutional Neural Networks (CNNs)
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#24 Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM)
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#25 Generative Adversarial Networks (GANs)
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#26 Transfer Learning and Fine-Tuning
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#27 Optimization Techniques in Deep Learning
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#28 Deep Reinforcement Learning
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#29 Ethical Considerations in Deep Learning
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#30 Future Trends in Deep Learning
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Unit Outline 60h

Learning Objectives

5 objectives
  • Understand fundamental concepts and architectures of deep learning models including neural networks, CNNs, RNNs, and GANs.
  • Develop proficiency in training deep learning models using various optimization and regularization techniques.
  • Analyze and apply transfer learning and fine-tuning methods to improve model performance on diverse tasks.
  • Evaluate ethical considerations and societal impacts related to the deployment of deep learning technologies.
  • Explore emerging trends and future directions in deep learning including reinforcement learning and quantum computing.

Content Outline

Preview

Unit 751: Advanced Deep Learning

1. Introduction to Deep Learning

  • Definition and scope of deep learning
  • Comparison with traditional machine learning techniques
  • Key concepts: neural networks, layers, and hierarchical feature learning
  • Applications across industries

2. Artificial Neural Networks (ANNs)

2.1 Structure and Components

  • Perceptrons: basic building blocks
  • Layers: input, hidden, output
  • Weights and biases

2.2 Activation Functions

  • Purpose and role in networks
  • Popular functions: ReLU, Sigmoid, Tanh
  • Properties and use cases

2.3 Training Mechanisms

  • Forward propagation
  • Loss functions: Mean Squared Error, Cross-Entropy, others
  • Optimization algorithms: Gradient Descent, Stochastic Gradient Descent
  • Backpropagation algorithm: theory and implementation

3. Convolutional Neural Networks (CNNs)

3.1 Architecture

  • Convolutional layers: filters, stride, padding
  • Pooling layers: max pooling, average pooling
  • Fully connected layers
  • Famous architectures: AlexNet, ResNet

3.2 Applications

  • Image recognition
  • Object detection
  • Image segmentation

4. Recurrent Neural Networks (RNNs) and Variants

4.1 RNN Fundamentals

  • Sequence data handling
  • Vanishing and exploding gradient problems

4.2 Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU)

  • Memory cells and gating mechanisms
  • Advantages over vanilla RNNs

4.3 Applications

  • Natural language processing
  • Speech recognition
  • Time series analysis

5. Generative Adversarial Networks (GANs)

5.1 Structure

  • Generator network
  • Discriminator network
  • Adversarial training process

5.2 Applications

  • Realistic image generation
  • Video synthesis
  • Text generation
  • Style transfer

6. Transfer Learning and Fine-Tuning

  • Concept of transfer learning
  • Using pre-trained models
  • Strategies for fine-tuning on new datasets
  • Benefits: improved performance and reduced training time

7. Optimization Techniques in Deep Learning

  • Advanced optimization methods: Adam, RMSProp
  • Learning rate scheduling
  • Regularization techniques: dropout, L2 regularization
  • Batch normalization
  • Techniques to prevent overfitting

8. Deep Reinforcement Learning

  • Basics of reinforcement learning
  • Q-learning and policy gradients
  • Integration with deep learning
  • Applications in gaming, robotics, and decision-making

9. Ethical Considerations in Deep Learning

  • Bias and fairness in AI models
  • Data privacy and security
  • Transparency and explainability
  • Accountability and responsible AI practices
  • Mitigation strategies for ethical risks

10. Future Trends in Deep Learning

  • Explainable AI (XAI)
  • Self-supervised and meta-learning
  • Attention mechanisms and transformers
  • Impact of quantum computing on deep learning
  • Emerging research directions
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Bachelor of Science in Artificial Intelligence

This unit is an integral part of the Bachelor of Science in Artificial Intelligence syllabus.

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