NumPy 教程

• Determining Moore’s Law with real data in NumPy
  • What you’ll do
  • Skills you’ll learn
  • What you’ll need
  • Building Moore’s law as an exponential function
  • Loading historical manufacturing data to your workspace
  • Calculating the historical growth curve for transistors
  • Sharing your results as zipped arrays and a csv
    • Zipping the arrays into a file
    • Creating your own comma separated value file
  • Wrapping up
  • References
• Saving and sharing your NumPy arrays
  • What you’ll learn
  • What you’ll do
  • What you’ll need
  • Create your arrays
  • Save your arrays with NumPy’s savez
  • Remove the saved arrays and load them back with NumPy’s load
  • Reassign the NpzFile arrays to x and y
  • Success
  • Another option: saving to human-readable csv
  • Rearrange the data into a single 2D array
  • Save the data to csv file using savetxt
  • Our arrays as a csv file
  • Success, but remember your types
  • Wrapping up
• Analyzing the impact of the lockdown on air quality in Delhi, India
  • What you’ll do
  • What you’ll learn
  • What you’ll need
  • The problem of air pollution
  • Building the dataset
  • Calculating the Air Quality Index
    • Moving averages
    • Sub-indices
    • Air quality indices
  • Paired Student’s t-test on the AQIs
    • Sampling
    • Defining the hypothesis
    • Calculating the test statistics
  • What do the t and p values mean?
  • In practice…
  • Further reading
• Deep learning on MNIST
  • Prerequisites
  • Table of contents
  • 1. Load the MNIST dataset
  • 2. Preprocess the data
    • Convert the image data to the floating-point format
    • Convert the labels to floating point through categorical/one-hot encoding
  • 3. Build and train a small neural network from scratch
    • Neural network building blocks with NumPy
    • Model architecture and training summary
    • Compose the model and begin training and testing it
  • Next steps
• Deep reinforcement learning with Pong from pixels
  • Prerequisites
  • Table of contents
    • A note on RL and deep RL
    • Deep RL glossary
  • Set up Pong
  • Preprocess frames (the observation)
  • Create the policy (the neural network) and the forward pass
  • Set up the update step (backpropagation)
  • Define the discounted rewards (expected return) function
  • Train the agent for a number of episodes
  • Next steps
  • Appendix
    • Notes on RL and deep RL
    • How to set up video playback in your Jupyter notebook
• Masked Arrays
  • What you’ll do
  • What you’ll learn
  • What you’ll need
  • What are masked arrays?
  • When can they be useful?
  • Using masked arrays to see COVID-19 data
  • Exploring the data
  • Missing data
  • Fitting Data
  • In practice
  • Further reading
    • Reference
• Plotting Fractals
  • What you’ll do
  • What you’ll learn
  • What you’ll need
  • Warmup
  • Julia set
  • Mandelbrot set
  • Generalizing the Julia set
    • Newton Fractals
  • Creating your own fractals
  • In conclusion
  • On your own
  • Further reading
• Determining Static Equilibrium in NumPy
  • What you’ll do:
  • What you’ll learn:
  • What you’ll need:
  • Solving equilibrium with Newton’s second law
  • Solving Equilibrium as a sum of moments
  • Finding values with physical properties
    • Another Example
  • Wrapping up
    • Additional Applications
    • References
• Linear algebra on n-dimensional arrays
  • Prerequisites
  • Learner profile
  • Learning Objectives
  • Content
    • Shape, axis and array properties
    • Operations on an axis
  • Approximation
    • Applying to all colors
    • Products with n-dimensional arrays
    • Final words
  • Further reading
• X-ray image processing
  • Prerequisites
  • Table of contents
  • Examine an X-ray with imageio
  • Combine images into a multidimensional array to demonstrate progression
  • Edge detection using the Laplacian-Gaussian, Gaussian gradient, Sobel, and Canny filters
    • The Laplace filter with Gaussian second derivatives
    • The Gaussian gradient magnitude method
    • The Sobel-Feldman operator (the Sobel filter)
    • The Canny filter
  • Apply masks to X-rays with np.where()
  • Compare the results
  • Next steps