JulienBeaulieu
  • Introduction
  • Sciences
    • Math
      • Probability
        • Bayes Rule
        • Binomial distribution
        • Conditional Probability
      • Statistics
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          • Normal Distributions
          • Sampling Distributions
          • Confidence Intervals
          • Hypothesis Testing
          • AB Testing
        • Simple Linear Regression
        • Multiple Linear Regression
          • Statistical learning course
          • Model Assumptions And How To Address Each
        • Logistic Regression
      • Calculus
        • The big picture of Calculus
          • Derivatives
          • 2nd derivatives
          • The exponential e^x
        • Calculus
        • Gradient
      • Linear Algebra
        • Matrices
          • Matrix Multiplication
          • Inverses and Transpose and permutations
        • Vector Space and subspaces
        • Orthogonality
          • Orthogonal Sets
          • Projections
          • Least Squares
        • Gaussian Elimination
    • Programming
      • Command Line
      • Git & GitHub
      • Latex
      • Linear Algebra
        • Element-wise operations, Multiplication Transpose
      • Encodings and Character Sets
      • Uncategorized
      • Navigating Your Working Directory and File I/O
      • Python
        • Problem Solving
        • Strings
        • Lists & Dictionaries
        • Storing Data
        • HTTP Requests
      • SQL
        • Basic Statements
        • Entity Relationship Diagram
      • Jupyter Notebooks
      • Data Analysis
        • Data Visualization
          • Data Viz Cheat Sheet
          • Explanatory Analysis
          • Univariate Exploration of Data
            • Bar Chart
            • Pie Charts
            • Histograms
            • Kernel Density Estimation
            • Figures, Axes, and Subplots
            • Choosing a Plot for Discrete Data
            • Scales and Transformations (Log)
          • Bivariate Exploration of Data
            • Scatterplots
            • Overplotting, Transparency, and Jitter
            • Heatmaps
            • Violin & Box Plots
            • Categorical Variable Analysis
            • Faceting
            • Line Plots
            • Adapted Bar Charts
            • Q-Q, Swarm, Rug, Strip, Stacked, and Rigeline Plots
          • Multivariate Exploration of Data
            • Non-Positional Encodings for Third Variables
            • Color Palettes
            • Faceting for Multivariate Data
            • Plot and Correlation Matrices
            • Other Adaptations of Bivariate PLots
            • Feature Engineering for Data Viz
        • Python - Cheat Sheet
    • Machine Learning
      • Courses
        • Practical Deep learning for coders
          • Convolutional Neural Networks
            • Image Restauration
            • U-net
          • Lesson 1
          • Lesson 2
          • Lesson 3
          • Lesson 4 NLP, Collaborative filtering, Embeddings
          • Lesson 5 - Backprop, Accelerated SGD
          • Tabular data
        • Fast.ai - Intro to ML
          • Neural Nets
          • Business Applications
          • Class 1 & 2 - Random Forests
          • Lessons 3 & 4
      • Unsupervised Learning
        • Dimensionality Reduction
          • Independant Component Analysis
          • Random Projection
          • Principal Component Analysis
        • K-Means
        • Hierarchical Clustering
        • DBSCAN
        • Gaussian Mixture Model Clustering
        • Cluster Validation
      • Preprocessing
      • Machine Learning Overview
        • Confusion Matrix
      • Linear Regression
        • Feature Scaling and Normalization
        • Regularization
        • Polynomial Regression
        • Error functions
      • Decision Trees
      • Support Vector Machines
      • Training and Tuning
      • Model Evaluation Metrics
      • NLP
      • Neural Networks
        • Perceptron Algorithm
        • Multilayer Perceptron
        • Neural Network Architecture
        • Gradient Descent
        • Backpropagation
        • Training Neural Networks
  • Business
    • Analytics
      • KPIs for a Website
  • Books
    • Statistics
      • Practice Statistics for Data Science
        • Exploring Binary and Categorical Data
        • Data and Sampling Distributions
        • Statistical Experiments and Significance Testing
        • Regression and Prediction
        • Classification
        • Correlation
    • Pragmatic Thinking and Learning
      • Untitled
    • A Mind For Numbers: How to Excel at Math and Science
      • Focused and diffuse mode
      • Procrastination
      • Working memory and long term memory
        • Chunking
      • Importance of sleeping
      • Q&A with Terrence Sejnowski
      • Illusions of competence
      • Seeing the bigger picture
        • The value of a Library of Chunks
        • Overlearning
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  • Deliberate Practice
  • Interleaving

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  1. Books
  2. A Mind For Numbers: How to Excel at Math and Science
  3. Seeing the bigger picture

Overlearning

When you're learning a new idea, for example a new vocabulary word or a new concept or a new problem solving approach, you sometimes tend to practice it over and over again during the same study session. A little of this is useful and necessary, but continuing to study or practice after you've mastered what you can in the session is called overlearning. Overleaning can have its place. It can produce an automaticity that can be important when you're executing a serve in tennis or a perfect piano concerto. If you choke on tests or public speaking, overlearning can be especially valuable.

but be wary of repetitive overlearning during a single session. Research has shown it can be a waste of valuable learning time.

The reality is, once you've got the basic idea down during a session, continuing to hammer away at it during the same session doesn't strengthen the kinds of long term memory connections you want to have strengthened. Worse yet, focusing on one technique is a little like learning carpentry by only practicing with a hammer.

Using a subsequent study session to repeat what you're trying to learn is just fine and often valuable. It can strengthen and deepen your chunked neuron patterns. But be wary; repeating something you already know perfectly well, is, face it, easy

Deliberate Practice

Instead, you want to balance your studies by deliberately focusing on what you find more difficult. This focusing on the more difficult material is called deliberate practice. It's often what makes the difference between a good student and a great student.

Interleaving

Once you have the basic idea of the technique down during your study session, sort of like learning to ride a bike with training wheels, start interleaving your practice with problems of different types or different types of approaches, concepts, procedures.

Mastering a new subject means learning not only the basic chunks, but also learning how to select and use different chunks. The best way to learn that is by practicing jumping back and forth between problems or situations that require different techniques or strategies. This is called interleaving.

In science and math in particular it can help to look ahead at the more varied problem sets that are sometimes found at the end of chapters. Or you can deliberately try to make yourself occasionally pick out why some problems call for one technique as opposed to another. You want your brain to become used to the idea that just knowing how to use a particular concept, approach, or problem-solving technique isn't enough. You also need to know when to use it. Interleaving your studies, making it a point to review for a test, for example, by skipping around through problems in the different chapters and materials can sometimes seem to make your learning a little more difficult, but in reality, it helps you learn more deeply.

Although practice and repetition is important in helping build solid neural patterns to draw on, it's interleaving that starts building flexibility and creativity. It's where you leave the world of practice and repetition, and begin thinking more independently.

Developing expertise in several fields means you can bring very new ideas from one field to the other, but it can also mean that your expertise in one field or the other isn't quite as deep as that of the person who specializes in only one discipline.

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