CS5720 - Week 6
Slide 120 of 120

Week 6 Summary & Week 7 Preview

🎯 Week 6 Key Concepts

  • Sequence Data
    Understanding temporal dependencies and order
  • Sequence Problem Types
    One-to-one, one-to-many, many-to-one, many-to-many
  • Feedforward Limitations
    No memory, fixed size, order independence
  • RNN Concept
    Recurrent connections and memory
  • RNN Architecture
    Hidden states, parameter sharing, unfolding
  • Forward Pass
    Sequential processing step by step
  • Training & Challenges
    BPTT, vanishing gradients, practical solutions
  • RNN Applications
    Text, time series, generation, real-world uses

🚀 Week 7 Preview

  • LSTM Networks
    Solving vanishing gradients with memory cells
  • GRU Networks
    Simplified LSTM with gating mechanisms
  • Bidirectional RNNs
    Processing sequences forward and backward
  • Deep RNNs
    Stacking RNN layers for complexity
  • Word Embeddings
    Word2Vec, GloVe, and semantic representations
  • Attention Mechanisms
    Focusing on relevant parts of sequences
  • Seq2Seq Models
    Advanced encoder-decoder architectures
  • Practical Implementation
    Building production-ready RNN systems

Your Deep Learning Journey

1-5
Foundations
→
6
RNN Basics
→
7
Advanced RNNs
→
8+
Specialized Topics
🧠
Sequential Thinking
You now understand how AI can process sequences and maintain memory
🔧
Practical Applications
You can identify when and how to use RNNs for real-world problems
🏗️
Architecture Mastery
You understand RNN structure and can design appropriate architectures
🚀
Strong Foundation
Ready to tackle advanced topics like LSTMs and Transformers
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Prepared by Dr. Gorkem Kar