Podcast
Questions and Answers
Which of the following is NOT a type of Artificial Neural Network mentioned in the content?
Which of the following is NOT a type of Artificial Neural Network mentioned in the content?
- Back-propagation Network
- Perceptron Networks
- Kohonen Self-Organizing Feature Maps
- Support Vector Machine (correct)
Back-propagation is a method used primarily for Unsupervised Learning Networks.
Back-propagation is a method used primarily for Unsupervised Learning Networks.
False (B)
What is the purpose of Dataset Augmentation in Deep Learning?
What is the purpose of Dataset Augmentation in Deep Learning?
To increase the size and diversity of the training dataset.
The ______ is a type of neural network that uses competitive learning to cluster input data into distinct categories.
The ______ is a type of neural network that uses competitive learning to cluster input data into distinct categories.
Match the following optimization strategies with their descriptions:
Match the following optimization strategies with their descriptions:
What is the main goal of using regularization techniques in Deep Learning?
What is the main goal of using regularization techniques in Deep Learning?
Name one application of large-scale deep learning.
Name one application of large-scale deep learning.
The ______ is a method in deep learning that involves adding noise to the input data to improve model robustness.
The ______ is a method in deep learning that involves adding noise to the input data to improve model robustness.
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Study Notes
Artificial Neural Networks
- Basic models of ANN
- Perceptron Networks
- Adaptive Linear Neuron
- Back-propagation Network
- Important terminologies
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Associative Memory Networks
- BAM (Bidirectional Associative Memory)
- Hopfield Networks
- Training algorithms for pattern association
Unsupervised Learning Networks
- Fixed Weight Competitive Nets
- Maxnet
- Hamming Network
- Kohonen Self-Organizing Feature Maps
- Learning Vector Quantization
- Counter Propagation Networks
- Adaptive Resonance Theory Networks
Introduction to Deep Learning
- Historical Trends in Deep learning
- Deep Feed-forward networks
- Gradient-Based learning
- Hidden Units
- Architecture Design
- Back-Propagation and Other Differentiation Algorithms
Regularization for Deep Learning
- Parameter norm Penalties
- L1 regularization
- L2 regularization
- Norm Penalties as Constrained Optimization
- Regularization and Under-Constrained Problems
- Dataset Augmentation
- Noise Robustness
- Semi-Supervised learning
- Multi-task learning
- Early Stopping
- Parameter Typing and Parameter Sharing
- Sparse Representations
- Bagging and other Ensemble Methods
- Dropout
- Adversarial Training
- Tangent Distance
- tangent Prop and Manifold
- Tangent Classifier
Optimization for Train Deep Models:
- Challenges in Neural Network Optimization
- Basic Algorithms
- Gradient Descent
- Stochastic Gradient Descent
- Parameter Initialization Strategies
- Xavier Initialization
- He Initialization
- Algorithms with Adaptive Learning Rates
- AdaGrad
- RMSProp
- Adam
- Approximate Second-Order Methods
- L-BFGS
- Optimization Strategies and Meta-Algorithms
- Simulated Annealing
- Genetic Algorithms
Applications
- Large-Scale Deep Learning
- Computer Vision
- Speech Recognition
- Natural Language Processing
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