Complete practice quiz on neural network fundamentals
Day 6: Backpropagation
High Priority
Focus:
Lecture 3 - Part 2
Tasks:
• Master chain rule and computational graphs
• Study matrix calculus for backpropagation
• Implement backpropagation algorithm from scratch
Deliverables:
Solve 10 backpropagation practice problems with full derivations
Day 7: Week 1 Review & Assessment
High Priority
Focus:
Comprehensive Week 1 Review
Tasks:
• Complete Week 1 comprehensive quiz (20 questions)
• Review all lecture notes and key concepts
• Practice with flashcards (30 minutes minimum)
Deliverables:
Quiz score ≥80% and updated consolidated study notes
Week 2: Advanced Architectures
Day 8: Dependency Parsing Theory
Medium Priority
Focus:
Lecture 4 - Part 1
Tasks:
• Study dependency grammar fundamentals
• Learn transition-based parsing algorithms
• Understand arc-standard and arc-eager systems
Deliverables:
Manually annotate 5 sample sentences with dependency relations
Day 9: Neural Dependency Parsers
Medium Priority
Focus:
Lecture 4 - Part 2
Tasks:
• Study neural dependency parser architecture
• Learn evaluation metrics: UAS (Unlabeled Attachment Score) and LAS (Labeled Attachment Score)
• Understand feature representations for parsing
Deliverables:
Complete dependency parsing exercises and calculate UAS/LAS scores
Day 10: RNN Fundamentals
High Priority
Focus:
Lecture 5 - Part 1
Tasks:
• Study RNN architecture and sequential processing
• Understand vanishing and exploding gradient problems
• Learn gradient clipping techniques
Deliverables:
Implement vanilla RNN from scratch with gradient calculations
Day 11: LSTM & GRU
High Priority
Focus:
Lecture 5 - Part 2
Tasks:
• Master LSTM gating mechanisms (forget, input, output gates)
• Study LSTM equations and cell state updates
• Learn GRU architecture and compare with LSTM
Deliverables:
Code LSTM implementation from scratch with all gate calculations
Day 12: Bidirectional RNNs & Applications
Medium Priority
Focus:
Advanced RNN Architectures
Tasks:
• Learn bidirectional RNN processing
• Explore NLP applications: sequence labeling, named entity recognition
• Study encoder-decoder architectures
Deliverables:
Build a sequence labeling model using bidirectional LSTM
Day 13: Comprehensive Review
High Priority
Focus:
Complete Course Review (Lectures 1-5)
Tasks:
• Review all 5 lectures systematically
• Practice with all flashcards (complete deck)
• Solve past questions and practice problems
Deliverables:
Complete all practice quizzes with explanations for incorrect answers
Day 14: Final Assessment
High Priority
Focus:
Final Comprehensive Exam
Tasks:
• Take final exam (30 questions, mixed difficulty)
• Complete self-evaluation questionnaire
• Write reflection essay on learning journey
Deliverables:
Final exam score ≥85% and 500-word reflection essay
Test your understanding of NLP concepts covered in Lectures 1-5. Select the best answer for each question.
1. What is the main idea behind distributional semantics?
A) Words are defined by their dictionary meanings
B) Words are represented by their context and co-occurrence patterns
C) Words are encoded using one-hot vectors
D) Words are classified by their grammatical roles
2. In the Skip-gram model, what does the model predict?
A) The center word given context words
B) Context words given the center word
C) The next word in a sequence
D) The part of speech of a word
3. What is the main advantage of GloVe over Word2Vec?
A) GloVe uses global corpus statistics and co-occurrence matrices
B) GloVe is faster to train
C) GloVe produces shorter word vectors
D) GloVe doesn't require negative sampling
4. What is the purpose of the sigmoid activation function in neural networks?
A) To introduce non-linearity and output values between 0 and 1
B) To speed up training
C) To prevent overfitting
D) To normalize input values
5. What problem does the ReLU activation function solve compared to sigmoid?
A) It mitigates the vanishing gradient problem
B) It produces probabilistic outputs
C) It normalizes the output
D) It prevents overfitting
6. What is backpropagation fundamentally based on?
A) The chain rule of calculus
B) Linear regression
C) Principal component analysis
D) Fourier transforms
7. In matrix calculus, what is the Jacobian?
A) A matrix of all first-order partial derivatives
B) The determinant of a matrix
C) The inverse of a gradient matrix
D) A diagonal matrix of eigenvalues
8. What is a dependency parse tree?
A) A tree structure showing grammatical relationships between words
B) A binary search tree of word frequencies
C) A decision tree for classification
D) A tree of word embeddings
9. What does UAS (Unlabeled Attachment Score) measure in dependency parsing?
A) The percentage of words with correct head attachments
B) The percentage of correctly labeled dependencies
C) The parsing speed
D) The model's memory usage
10. What is the key characteristic of Recurrent Neural Networks (RNNs)?
A) They maintain hidden states that capture information from previous time steps
B) They process all inputs simultaneously
C) They only work with fixed-length sequences
D) They use convolutional layers
11. What problem do LSTMs solve that vanilla RNNs struggle with?
A) The vanishing gradient problem in long sequences
B) Parallel processing of sequences
C) Reducing model size
D) Increasing training speed
12. How many gates does an LSTM cell have?
A) Two gates (forget and input)
B) Three gates (forget, input, and output)
C) Four gates
D) One gate
13. What is the purpose of negative sampling in Word2Vec?
A) To make training more efficient by approximating the softmax
B) To remove negative words from the vocabulary
C) To create negative word embeddings
D) To balance positive and negative sentiment
14. What is perplexity in language modeling?
A) A measure of how well a model predicts a sample (lower is better)
B) The number of parameters in the model
C) The training time of the model
D) The vocabulary size
15. What is the main advantage of bidirectional RNNs?
A) They capture context from both past and future time steps
B) They train twice as fast
C) They use less memory
D) They work better with short sequences
Interactive Flashcards
Click on any card to reveal the answer. Practice these key concepts regularly for better retention.
What is Word2Vec?
A neural network model that learns word embeddings by predicting context words (Skip-gram) or center words (CBOW) from surrounding words.
What is GloVe?
Global Vectors for Word Representation - a model that learns embeddings by factorizing a word co-occurrence matrix using global corpus statistics.
What is the Skip-gram objective function?
$J(\theta) = \frac{1}{T} \sum_{t=1}^{T} \sum_{-m \leq j \leq m, j \neq 0} \log P(w_{t+j} | w_t)$ - maximizes the log probability of context words given center words.
What is negative sampling?
An approximation technique that samples a small number of negative examples instead of computing softmax over the entire vocabulary, making training more efficient.
What is the vanishing gradient problem?
In deep networks or long sequences, gradients become extremely small during backpropagation, making it difficult to train early layers or capture long-term dependencies.
What is an LSTM?
Long Short-Term Memory - an RNN architecture with gates (forget, input, output) that can learn long-term dependencies by controlling information flow.
What is the LSTM cell state equation?
$c_t = f_t \odot c_{t-1} + i_t \odot \tilde{c}_t$ - combines forgotten old state with new candidate information.
What is a GRU?
Gated Recurrent Unit - a simpler alternative to LSTM with only two gates (reset and update), combining forget and input gates into one.
What is dependency parsing?
The task of analyzing the grammatical structure of a sentence by establishing relationships (dependencies) between words, typically represented as a tree.
What is UAS vs LAS?
Unlabeled Attachment Score (UAS) measures correct head attachments. Labeled Attachment Score (LAS) also requires correct dependency labels.
What is the RNN update equation?
$h_t = \tanh(W_{hh}h_{t-1} + W_{xh}x_t + b_h)$ - combines previous hidden state with current input.
What is perplexity?
A measure of how well a language model predicts a sample. Lower perplexity indicates better prediction. Calculated as 2^(entropy).
What is backpropagation?
An algorithm for computing gradients in neural networks by applying the chain rule recursively from output to input layers.
What is the distributional hypothesis?
"You shall know a word by the company it keeps" - words that occur in similar contexts tend to have similar meanings.
What is gradient descent?
An optimization algorithm that iteratively adjusts parameters in the direction of negative gradient to minimize a loss function.
What is the softmax function?
A function that converts a vector of real numbers into a probability distribution, commonly used in classification tasks.
What is the ReLU activation function?
Rectified Linear Unit: $f(x) = \max(0, x)$ - introduces non-linearity while mitigating vanishing gradients.
What is a bidirectional RNN?
An RNN that processes sequences in both forward and backward directions, capturing context from both past and future time steps.
What is the chain rule in calculus?
A method for computing derivatives of composite functions: $\frac{dy}{dx} = \frac{dy}{du} \cdot \frac{du}{dx}$ - fundamental to backpropagation.
What is word embedding?
A dense vector representation of words in continuous space where semantically similar words are close together.
What is an N-gram language model?
A probabilistic model that predicts the next word based on the previous N-1 words using conditional probability from corpus statistics.
What is the CBOW model?
Continuous Bag of Words - predicts the center word from surrounding context words by averaging context word vectors.
What is transition-based parsing?
A parsing approach that builds dependency trees incrementally using a sequence of actions (shift, left-arc, right-arc) on a stack and buffer.
What is the forget gate in LSTM?
$f_t = \sigma(W_f \cdot [h_{t-1}, x_t] + b_f)$ - decides what information to discard from the cell state.
What is the input gate in LSTM?
$i_t = \sigma(W_i \cdot [h_{t-1}, x_t] + b_i)$ - decides what new information to add to the cell state.
Study Tips & Best Practices
Time Management
• Set specific study times each day
• Use the Pomodoro technique (25 min study, 5 min break)
• Prioritize high-priority tasks first
• Track your study hours consistently
• Take breaks to maintain focus and retention
Active Learning
• Take detailed notes in your own words
• Implement algorithms from scratch before checking solutions
• Solve practice problems immediately after learning concepts
• Teach concepts to others or explain them aloud
• Create mind maps to visualize connections
Flashcard Strategy
• Review flashcards daily (spaced repetition)
• Focus on cards you find difficult
• Create your own flashcards for personalized learning
• Practice both directions (term to definition and vice versa)
• Use flashcards before bed for better retention
Note-Taking Methods
• Use the Cornell method for structured notes
• Highlight key formulas and equations
• Include examples and edge cases
• Review and revise notes within 24 hours
• Create summary sheets for each lecture
Exam Preparation
• Complete all practice quizzes before exams
• Simulate exam conditions (timed practice)
• Review incorrect answers thoroughly
• Focus on understanding, not memorization
• Get adequate sleep before exam day
Difficult Concepts
• Break down complex topics into smaller parts
• Use multiple resources (videos, papers, tutorials)
• Work through examples step-by-step
• Ask questions in study groups or forums
• Don't move forward until you understand fundamentals
Coding Practice
• Write code from scratch without copying
• Debug your own implementations
• Test with different inputs and edge cases
• Read and understand others' code
• Contribute to open-source NLP projects
Progress Tracking
• Mark tasks as complete daily
• Review your progress weekly
• Celebrate milestones and achievements
• Adjust your study plan if needed
• Keep a learning journal
Collaboration
• Join study groups or online communities
• Discuss concepts with peers
• Share resources and insights
• Participate in code reviews
• Attend office hours or discussion sessions