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Recurrent Neural Networks (RNNs) and Word2Vec. ocdevel.com/mlg/22 for notes and resources

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Natural Language Processing classical/shallow algorithms. ocdevel.com/mlg/20 for notes and resources

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Natural Language Processing classical/shallow algorithms. ocdevel.com/mlg/19 for notes and resources

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Introduction to Natural Language Processing (NLP) topics. ocdevel.com/mlg/18 for notes and resources

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At this point, browse #importance:essential on ocdevel.com/mlg/resources with the 45m/d ML, 15m/d Math breakdown.

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Full notes at  ocdevel.com/mlg/16 

Inspiration in AI Development

Early inspirations for AI development centered around solving challenging problems, but recent advancements like self-driving cars and automated scientific discoveries attract professionals due to potential economic automation and career opportunities.

The Singularity

The singularity suggests exponential technological growth leading to a point where AI and robotics automate all technology development, potentially achieving 'seed AI' capable of self-improvement and escaping human intervention.

Defining Consciousness

Consciousness distinguishes intelligence by awareness. Perception, self-identity, learning, memory, and awareness might all contribute to consciousness, but awareness or subjective experience (quaia) is viewed as a core component.

Hard vs. Soft Problems of Consciousness

The soft problems are those we know through sciences — like brain regions being associated with specific functions. The hard problem, however, is explaining how subjective experience arises from physical processes in the brain.

Theories and Debates

• Emergence: Consciousness as an emergent property of intelligence.
• Computational Theory of Mind (CTM): Any computing device could exhibit consciousness as it processes information.
• Biological Plausibility vs. Functionalism: Whether AI must biologically resemble brains or just functionally replicate brain output.

The Future of Artificial Consciousness

Opinions vary widely on whether AI can achieve consciousness, depending on theories around biological plausibility and arguments like John Searl's Chinese Room. The matter of consciousness remains deeply philosophical, touching on human identity itself. The expansion of machine learning and AI might be humanity's next evolutionary step, potentially culminating in the creation of conscious entities.

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Full notes at  ocdevel.com/mlg/15 

Concepts

• Performance Evaluation Metrics: Tools to assess how well a machine learning model performs tasks like spam classification, housing price prediction, etc. Common metrics include accuracy, precision, recall, F1/F2 scores, and confusion matrices.
• Accuracy: The simplest measure of performance, indicating how many predictions were correct out of the total.
• Precision and Recall:
  • Precision: The ratio of true positive predictions to the total positive predictions made by the model (how often your positive predictions were correct).
  • Recall: The ratio of true positive predictions to all actual positive examples (how often actual positives were captured).

Performance Improvement Techniques

• Regularization: A technique used to reduce overfitting by adding a penalty for larger coefficients in linear models. It helps find a balance between bias (underfitting) and variance (overfitting).
• Hyperparameters and Cross-Validation: Fine-tuning hyperparameters is crucial for optimal performance. Dividing data into training, validation, and test sets helps in tweaking model parameters. Cross-validation enhances generalization by checking performance consistency across different subsets of the data.

The Bias-Variance Tradeoff

• High Variance (Overfitting): Model captures noise instead of the intended outputs. It's highly flexible but lacks generalization.
• High Bias (Underfitting): Model is too simplistic, not capturing the underlying pattern well enough.
• Regularization helps in balancing bias and variance to improve model generalization.

Practical Steps

• Data Preprocessing: Ensure data completeness and consistency through normalization and handling missing values.
• Model Selection: Use performance evaluation metrics to compare models and select the one that fits the problem best.

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Full notes at ocdevel.com/mlg/14

Anomaly Detection Systems

• Applications: Credit card fraud detection and server activity monitoring.
• Concept: Identifying outliers on a bell curve.
• Statistics: Central role of the Gaussian distribution (normal distribution) in detecting anomalies.
• Process: Identifying significant deviations from the mean to detect outliers.

Recommender Systems

• Types:
  • Content Filtering: Uses features of items (e.g., Pandora’s Music Genome Project).
  • Collaborative Filtering: Based on user behavior and preferences, like "Users Also Liked" model utilized in platforms like Netflix and Amazon.
• Applications in Machine Learning: Linear regression applications in recommender systems for predicting user preferences.

Markov Chains

• Explanation: Series of states with probabilities dictating transitions to next states; present state is sufficient for predicting next state (Markov principle).
• Use Cases: Often found in reinforcement learning and operations research.
• Monte Carlo Simulation: Running simulations to determine the expected value or probable outcomes of Markov processes.

Resource

• Andrew NG's Coursera Course - Week 9: Focuses on anomaly detection and recommender systems.

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Full notes at  ocdevel.com/mlg/13 

Support Vector Machines (SVM)

• Purpose: Classification and regression.
• Mechanism: Establishes decision boundaries with maximum margin.
• Margin: The thickness of the decision boundary, large margin minimizes overfitting.
• Support Vectors: Data points that the margin directly affects.
• Kernel Trick: Projects non-linear data into higher dimensions to find a linear decision boundary.

Naive Bayes Classifiers

• Framework: Based on Bayes' Theorem, applies conditional probability.
• Naive Assumption: Assumes feature independence to simplify computation.
• Application: Effective for text classification using a "bag of words" method (e.g., spam detection).
• Comparison with Deep Learning: Faster and more memory efficient than recurrent neural networks for text data, though less precise in complex document understanding.

Choosing an Algorithm

• Assessment: Evaluate based on data type, memory constraints, and processing needs.
• Implementation Strategy: Apply multiple algorithms and select the best-performing model using evaluation metrics.

Links

• Andrew Ng Week 7
• Pros/cons table for algos
• Sci-Kit Learn's decision tree for algorithm selection.
• Machine Learning with R book for SVMs and Naive Bayes.
• "Mathematical Decision-Making" great courses series for Bayesian methods.

Podcast is still alive, working on next episode - sorry for the delay!

Sorry guys, had an intense last 3 weeks. Mid-move to Portland OR my employer folded so I had to hunt, stat (new expenses & all). Luckily I found a job in NLP, my favorite branch of ML! Big-time silver lining. BTW, everyone these days does whiteboarding. Used to be the top-dogs, now it's everyone. If you're ever on the hunt, read CtCI first (http://amzn.to/2nu6ya1). Essential.