Electron microscopy
  
Python Automation and Machine Learning for ICs: Chapter G
- Python Automation and Machine Learning for ICs -
- An Online Book: Python Automation and Machine Learning for ICs by Yougui Liao -
Python Automation and Machine Learning for ICs                                                                http://www.globalsino.com/ICs/        


Table of Contents/Index 
Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix

=================================================================================

 

 
                                                       
Comparison between Clouds (Amazon, IBM, Google ...) Introduction
Image generation with ML Introduction
Directed Acyclic Graph (DAG) Introduction
Hadoop MapReduce used by Google, Netflix, Amazon and Machine Learning Introduction
n-grams Introduction
Context-free grammar (CFG) Introduction
Formal grammar Introduction
GPT (Generative Pre-trained Transformer) Introduction
XGBoost (Extreme Gradient Boosting) Introduction
AutoML (Automated Machine Learning) versus Generative AI Introduction
Global import Introduction
Global variables Introduction
Guess and check algorithm in Python with a combination of a for loop and an if statement Introduction
Guido van Rossum Introduction
Goal State and Goal Test in ML Introduction
Trade-off between exploration and exploitation, and epsilon(ε-) greedy exploration Introduction
Grid world navigation Introduction
Maximum Likelihood Estimation (MLE) of single Gaussian (normal) distribution Introduction
Mixture of Gaussians (MoG) versus Factor Analysis (FA) Introduction
Expectation-Maximization (EM) algorithm working in Gaussian Mixture Models (GMMs) Introduction
Exploding gradients in ML Introduction
Vanishing gradients in ML Introduction
Batch Gradient Descent (BGD), Stochastic Gradient Descent (SGD), Mini-Batch Gradient Descent, Batch Stochastic Gradient Descent, Momentum, (Adagrad, Adadelta, RMSprop), and Adam (Adaptive Moment Estimation) Introduction
Gradient Boosting Introduction
Comparison among Grid Search, Bayesian Optimization, Random Search and Manual Search Introduction
Gini Loss Introduction
Gini impurity Introduction
Generalization risk/generalization error versus empirical risk Introduction
Generalization Introduction
Tricks for learning three dimensional geometry Introduction
Geometric Margin versus Functional Margin Introduction
Geometric margin in ML Introduction
Single Naive Bayes (Gaussian Naive Bayes) versus Multinomial Naive Bayes Introduction
Comparison between Poisson distribution, Gaussian (normal) distribution and logistic regression Introduction
Logistic regression versus Gaussian discriminant analysis Introduction
Discriminative algorithms versus generative models Introduction
Gaussian Discriminant Analysis (GDA) Introduction
GLM (Generalized Linear Model) Introduction
Gaussian distribution and standard gaussian distribution (multivariate normal distribution) Introduction
Tricks in Python Programming and principles and practices in good programming Introduction
Exponential Family: Parameter, Sufficient Statistic, Natural Parameter, Base Measure and Log-Partition Function (Bernoulli distribution and Gaussian distribution) Introduction
Probability density function (PDF): comparisons between (normal (gaussian) distribution, uniform distribution, exponential distribution and poisson distribution) Introduction
Generative learning models Introduction
Newton's method versus gradient descent Introduction
Update parameters θj using gradient of the loss function Introduction
Mixture of Gaussians (MoG) Introduction
Algorithms for directly finding the global optimum Introduction
Global optimization and global minimum Introduction
Plot diagram/graph Introduction
   
Gradient descent algorithm (for updating θ) Introduction
◆ Batch gradient descent Introduction
◆ Stochastic gradient descent (SGD) Introduction
◆    
   
Generalization Error/generalization risk/Generalization Loss/Test Error/Expected Error of Hypothesis/Risk Introduction
Common Words for Classification of Groups of Texts Introduction
GPUs/CPUs Introduction
Find nearest white pixel to a given/specifical pixel location on an binary image Introduction
pd.get_dummies (Code)
tf.Graph() Introduction
Graphlab Create Introduction
   
Google Introduction
◆  Google Translate Introduction
◆ Leetcode for Google/Amazon Introduction
◆ Comparisons among Manual Search, Vertex Vizier, AutoML and Early stopping on google cloud Introduction
◆ Google's Edge TPU hardware Introduction
◆ Methods to open google chrome (problems: Google chrome closes immediately after being launched with selenium) Introduction
◆ Google auto-search with Python Introduction
◆ googlecoursera/console.cloud.google Introduction
◆ GoogleNews-vectors-negative300.bin; A pre-trained model. Use "7zFM.exe" to unzip from .bin.gz format to .bin format. (code)
◆ Output the web links obtained by Google Search (from googlesearch import search) Introduction
◆ Save contents (download pdf files) in the webpages obtained by Google search into a text file Introduction
◆ Managing machine learning projects with Google Cloud Introduction
◆ Google Cloud Platform (GCP) versus Apache Hadoop Introduction
◆ Google Cloud Shell Introduction
◆ Google Kubernetes Engine (GKE) Introduction
◆ Specialized tools and APIs lacking in GCP (Google Cloud) for semiconductor applications  Introduction
◆ Google Cloud Natural Language API Introduction
   
Graph  
◆ plotly.graph_objects Introduction
   
   
Get the name of the current/most front window Introduction
Check to see if or get a window with a name containing specific titles or texts Introduction
Get the latest/newest/most recent file in a folder Introduction
Generic (or generalized) robot programming (GRP) Introduction
Graphical user interface (GUI) Introduction
Get a specific output for every input code.
Calculations in DataFrame: Add a column, calculate for a new column, delete a column, all the rows with values greater than 30 in "Score A" column Introduction
Pint summary of the statistic data, change data format, sort/group columns Introduction
Set the output image to zero everywhere except my mask (color filter), and display red, green, and blue (RGB) channels of an image. code, code.
Creates an image from an colored images after remove the grey components (image conversion from color to gray involved) code
Extract three blue, green, red images from a color image or grey image, or convert RGB (color) image into three blue, green, red images code
Extract three blue, green, red images from a color image or grey image, or convert RGB (color) image into three blue, green, red images code
Calculate the coordinates of a point in a given rectangle and the distance of a given point to a line code
Change/convert a colored image to a grey image(, and then show pixel values). cv2, cv2, cv2/skimage. cv2/skimage. PIL. matplotlib.
Create images with global, adaptive mean, adaptive Gaussian, binary, trunc, Tozero, and tozero thresholds. code
Find the greatest of three numbers code1, code2, code3.
Reverse the digits of a given number code
Get the list of the methods for a function Introduction
Get/list immediate subdirectories/subfolders; get only the last part of a path Introduction
Print and set file path as a variable (e.g. convert all characters in the pathname to lowercase) Introduction
matplotlib.pyplot axis/text color (xticks, rotation, xlabel, ylabel, title, fontsize, grid(), legend(), show()) Introduction
Global access to a variable inside a function from outside of the function Introduction
Get mouse position/coordinates on click Introduction
Get pixel location/coordinates on an image using mouse click/events Introduction
Generate text file with the bank of collecting all words, characters and strings from news Introduction
Global defects and local defects identified by defect denoising Introduction
Plot graph/figure/image from CSV file Introduction
Plot graph/figure/image from CSV file/DataFrame by removing/hiding blank/empty cells with axis range (plt.xlim()) Introduction
Plot data into the same graph/figure/image from different csv files Introduction
Plot multiple datasets on the same scatter graph with different x- and y-axis values Introduction
Calculating the area fraction of each circle overlapped by a square grid and build wafer map Introduction
Get username and password with getpass Introduction
Get header/column names from DataFrame Introduction
Get directory/path/file name partially Introduction
Python modules to interact with the operating system (os, platform, subprocess, shutils, glob and sys) Introduction
Pyvis: An interactive geometric graph network/link/landscape Introduction
Modify HTML webpage (e.g. with graph network by adding text/hyperlink in) Introduction
Trick: generic code/script templates for complex automation Introduction
Summary/templates of plotting graphs/figures Introduction
Generative Adversarial Network (GAN) technologies Introduction
Find repeating patterns in columns, group them as cycles, and column correlations Introduction
Classification of groups of texts Introduction
__add__, __call__, __contains__, __delitem__, __delattr__, __eq__, __enter__, __ge__, __getattribute__, __getnewargs__, __getattr__, __getitem__, __gt__, __hash__, __reduce__, __iadd__, __imul__, __init_subclass__, __index__, __int__, __invert__, __new__, __neg__, __reduce_ex__, __reversed__, __rmul__, __radd__, __rand__, __rdivmod__, __rfloordiv__, __rlshift__, __rmod__, __ror__, __round__,__rpow__, __rrshift__, __rshift__, __rsub__, __rtruediv__, __rxor__, __dir__, __doc__, __divmod__, __iter__, __le__, __lt__, __len__, __ne__, __repr__, __setattr__, __setitem__, __sizeof__, __lshift__, __sub__, __subclasshook__, __str__ Introduction
Comparison between decision tree, random forest and XGBoost (extreme gradient boosting) Introduction
.groupby('...')['...']: sort/group columns. (code). CSV: (code) Introduction
Generating heatmaps for grouped data occurrences Introduction
Save dynamic graph as a movie/video or split a movie to image frames Introduction

 

 

   

 
.get_option() CSV: Get the value of a single option. (code)
global/globals   Introduction.
sct.grab() (code)
.getpixel() code.
.geometry Window size (width and length). Introduction. code. code.
.get() A switch statement. code. (code).
os.getcwd() Get current working directory/path.
win32clipboard.GetClipboardData() code.
requests.get Introduction. code. code.
__get__/method-wrapper name/type: implementation of the read-only descriptor protocol (see XREF)
__globals__/dict name/type: global variables of the module where the function is defined
skimage.measure.grid_points_in_poly Test whether points on a specified grid are inside a polygon.
Gensim (code). Gensim is an open source python library for natural language processing and is for topic modelling, document indexing, which means it is able to extract the underlying topics from a large volume of text. It can handle large text files without loading the entire file in memory. Gensim library will enable us to develop word embedding by training our own word2vec models on a custom corpus either with CBOW of skip grams algorithms. The Gensim was developed and is maintained by the Czech natural language processing researcher Radim Řehůřek and his company RaRe Technologies.
Word2Vec with Gensim Introduction
glob Unix style pathname pattern expansion. code.
glob.glob(pathname, *, recursive=False)

Return a possibly-empty list of path names that match pathname, which must be a string containing a path specification. pathname can be either absolute (like /.../Makefile) or relative (like ../../*/*.tft), and can contain shell-style wildcards. Broken symlinks are included in the results (as in the shell). Whether or not the results are sorted depends on the file system. If a file that satisfies conditions is removed or added during the call of this function, whether a path name for that file be included is unspecified. If recursive is true, the pattern “**” will match any files and zero or more directories, subdirectories and symbolic links to directories. If the pattern is followed by an os.sep or os.altsep then files will not match. Introduction. code. (code)

"gray" Convert a GRB image to a grey image. code.
grid Display axis grid (on by default)
grid() (code)
t.getscreen() Return the TurtleScreen object the turtle is drawing on. (code)
t.getcanvas() Return the Canvas of this TurtleScreen. (code)
Collections of geometric shape (code)
.goto()

.goto(x, y=None) x – a number or a pair/vector of numbers; y – a number or None. (code)

.getcanvas().postscript(file="") Picture saved in current working directory. (code)
GetKeyState(VK_NUMLOCK) Turn off or on Num Lock. .press("numlock") (code).
GetKeyState(VK_CAPITAL) Caps Lock, .press("capslock"). (code).
get_attribute('href') (code)
win32api.GetCursorPos() (code)
hotkey('g') Introduction
.get_column_letter() (code)
.getAllTitles() Get all the Python program windows, *IDLE Shell window, e.g. *IDLE Shell 3.9.5, the most front window on Dreamweaver, the most front webpage on each Chrome window, the name of each opened applications, e.g. DigitalMicrograph. Introduction
.getWindowsWithTitle() Returns a list of Window objects for every visible window that includes the string in its title bar. Introduction
.getAllWindows() (code)
.getActiveWindow() (code)
.getActiveWindow() (code)
.getWindowsAt() (code)
.GetForegroundWindow() (code)
.GetWindowText() (code)
.GetWindowRect() Get the position and the size of a window. Introduction
FLAT, RAISED, SUNKEN, GROOVE and RIDGE in Tkinter button relief styles Introduction
glob.iglob() (code)
os.path.getctime (code)
group_id= (code).
driver.get(“http://globalsino.com”) Navigate to URL
 
GeoPandas

Introduction.

os.path.getatime() (code)
os.path.getmtime() (code)
os.path.getsize() (code)
cv2.getRotationMatrix2D() (code)
go.Figure (Code)
go.Heatmap (Code)

 

 

 

=================================================================================