Electron microscopy
Python Automation and Machine Learning for ICs: Chapter B
- 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



Four/five V of big data Introduction
Evaluating a ML model with BigQuery ML Introduction
Building an effective machine learning team Introduction
Principles of ethical and responsible ML (selection bias, confirmation bias, automation bias, model fairness) Introduction
Best practices for implementing ML in semiconductor manufacturing Introduction
Identifying the business value of using ML Introduction
Big data lifecycle Introduction
XGBoost (Extreme Gradient Boosting) Introduction
BigQuery ML and Python-based ML frameworks Introduction
Python libraries for Bayesian ML techniques Introduction
BigQuery ML Introduction
Comparison between Naive Bayes algorithms and Bayesian machine learning techniques Introduction
Biological (human brain) and AI neural networks Introduction
Bisection search (binary search) Introduction
Branching Introduction
Code blocks Introduction
Mistakes that beginner machine learning (ML) students often make Introduction
Backtracking search Introduction
Binary constraint Introduction
Bayesian ML techniques Introduction
Knowledge base (repository) Introduction
Uninformed search/blind search algorithms Introduction
Breadth-First Search (BFS) Introduction
Bellman expectation and Bellman optimality equations Introduction
Reinforcement learning       
Electroencephalogram cap (EEG cap) for brain Introduction
Bayesian networks Introduction
(Forward and backward) propagation equations 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
Neural network vs. end-to-end learning vs. black box model
AdaBoost (Adaptive Boosting) Model Introduction
Boosting versus Bagging Introduction
Boosting in ML Introduction
Regular decision trees and decision trees with bagging Introduction
Bagging in decision trees Introduction
Bagging (Bootstrap Aggregating) Introduction
Gradient Boosting Introduction
Blackbox optimization algorithms Introduction
Comparison among Grid Search, Bayesian Optimization, Random Search and Manual Search Introduction
Bayesian optimization Introduction
Batch sizes Introduction
Bayes error/Bayes risk/Bayes rate/irreducible error Introduction
Frequentist approach versus Bayesian approach Introduction
Bias and variance, and bias-variance trade-off in ML Introduction
Comparison among classifier, hyperplane and decision boundary Introduction
Hyperplane/Decision Boundary in ML Introduction
Multivariate Bernoulli learning model Introduction
Bernoulli distribution Introduction
Exponential Family: Parameter, Sufficient Statistic, Natural Parameter, Base Measure and Log-Partition Function (Bernoulli distribution and Gaussian distribution) Introduction
Bayesian Probability, Bayesian Statistics (Distribution Over a Distribution), versus Bayesian Inference Introduction
Bandwidth parameter (τ) in LWR and KDE Introduction
Batch gradient descent Introduction
Practice ML projects for beginners Introduction
Binary trees Introduction
Big O notation Introduction
Point-Biserial Correlation Introduction
Brute force discretization Introduction
Union Bound/Boole's Inequality Introduction
Deviation Probability (Hoeffding Bound) Introduction
Sample Size versus Bounds Introduction
Probability bounds analysis (PBA) Introduction
Bound in math/ML Introduction
Similarity-based clustering method (SCM) Introduction
Send a variable from one script (back) to another script with a function Introduction
BERTScore/BERT (Bidirectional Encoder Representations from Transformer) Introduction
Built-ins/Builtins Commands in Python Introduction
Build own/customized keyword candidates Introduction
Overfitting and underfitting Introduction
Create a Batch File to Run a Python Script Introduction
pyodbc for bridging SQL to Python Introduction
.bat (batch) files for Command Prompt Windows Introduction
Automatically restart script execution after it breaks/fails/error Introduction
Plot graph/figure/image from CSV file/DataFrame by removing/hiding blank/empty cells with axis range (plt.xlim()) Introduction
Plot confidence bands Introduction
Compare dates (x days after or before a date), and difference between two dates in days Introduction
BaseException Introduction
Extract substrings between brackets (including brackets) Introduction
Get username and encoded password with getpass or or base64 Introduction
(Single) Naive Bayes/Gaussian Naive Bayes Introduction
Bayes' theorem (Bayes rule or Bayes law) in machine learning Introduction
Multinomial Naive Bayes algorithm Introduction
Random (Bootstrap) Forests Introduction
Box and Whisker plots Introduction
Built-in functions in Python Introduction
Add letter/commas/numbers/characters to the end/beginning of strings in a list Introduction
Replace the lines between two lines “xx” and “yy” in a text file with new lines Introduction
Remove string 0s from the back/end of a list until non-zero values Introduction



borderwidth= (code)
break a functions to exit a loop Stop the loop, e.g. while loop immediately. Introduction. Normally do not use it, but a break statement is needed if it’s possible to end a loop w/o the condition being false. code1, code2
.bold (code)
bool() Applies the standard truth testing procedure to the passed object/value and returns a boolean value, that is, converting a value to Boolean (True or False). The object will always return True, unless: the object is empty, like [], (), {}; the object is False; the object is 0; the object is None. code. (code).
__bool__() If for any object x, the __bool__() method is not defined, we get True as a result.
watchdog.utils.bricks.SkipRepeatsQueue Thread-safe event queue based on a special queue that skips adding the same event (FileSystemEvent) multiple times consecutively. Thus avoiding dispatching multiple event handling calls when multiple identical events are produced quicker than an observer can consume them.
watchdog.utils.BaseThread Producer thread base class subclassed by event emitters that generate events and populate a queue with them.
Parameters: event_queue (watchdog.events.EventQueue) – The event queue to populate with generated events.
watch (ObservedWatch) – The watch to observe and produce events for.
timeout (float) – Timeout (in seconds) between successive attempts at reading events.
watchdog.utils.BaseThread Consumer thread base class subclassed by event observer threads that dispatch events from an event queue to appropriate event handlers.
Parameters: timeout (float) – Event queue blocking timeout (in seconds).
class watchdog.observers.api.BaseObserver(emitter_class, timeout=1)
watchdog.observers.api.BaseObserver Platform-independent observer that polls a directory to detect file system changes.
watchdog.observers.api.BaseObserver File system independent observer that polls a directory to detect changes.
class watchdog.utils.BaseThread
Button() Introduction
scipy.linalg.block_diag Create a block diagonal matrix from the provided arrays.
Binary I/O, io.BytesIO f = open("myfile.jpg", "rb"), f = io.BytesIO(b"some initial binary data: \x00\x01"). Code.
skimage.measure.block_reduce(image, block_size) Down-sample image by applying function to local blocks.
BeautifulSoup Introduction. Has an excellent XML- and HTML- parsing library for beginners, but it may be a bit slow.
Bob Is developed at Idiap Research Institute in Switzerland, Bob is a free signal processing and machine learning toolbox. The toolbox is written in a mix of Python and C++. From image recognition to image and video processing using machine learning algorithms, a large number of packages are available in Bob to make all of this happen with great efficiency in a short time.
Bokeh A Data Visualisation library for Python, Bokeh allows interactive visualisation. It makes use of HTML and Javascript to provide graphics, making it reliable for contributing web-based applications. It is highly flexible and allows you to convert visualisation written in other libraries such as ggplot or matplotlib. Bokeh makes use of straight-forward commands to create composite statistical scenarios.
v2.rectangle(image, start_point, end_point, color of border line, border thickness) border. Compute the bounding box of the contour and then draw the bounding box on an image to represent where the ROI is. code. code. code.
.backward() (code)
t.begin_fill() To be called just before drawing a shape to be filled. (code)
from pynput.mouse import Button (code)
.press(Button.left) (code)
.release(Button.left) (code)
.click(Button.left, x) x clicks of mouse. (code)
.press(Button.right) (code)
.release(Button.right) (code)
.bottomright (code)
.bind() Introduction
'<Button-1>' Single left click. (code)
highlightbackground= (code)
background= (code)
.buttons= (code)
.button_pressed() (code)
.top/.bottom/.left/.right Introduction
basicConfig (code).
background_color= (code).
driver.navigate().back() Navigate backward in browser history
os.path.basename() (code).
Color in table obtained by matplotlib.pyplot/change background color of cells in table Introduction
Mirror/reflect image from left to right/from top to bottom Introduction
Plot horizontal stacked bar/histogram Introduction
Training process in ML (with "best"-option table) Introduction
Predictive/predict model (with "best"-option table) Introduction
Median blurring and cv2.medianBlur() Introduction
.pack(side=LEFT)/.pack(side=RIGHT)/.place(x=, y=) --- position of the buttons (Code)
Locate/find the center/coordinates of a bright (maximum/highest intensity) spot in an image & find nearest white pixel to a given/specifical pixel location on an binary image Introduction
find_element(By.XPATH, "") Introduction
Add padding/black/colored edge to images Introduction
Reasons (benefits) of automation and how to start Introduction
Browser-based visualization tool Introduction
Dummy variables/binary variables Introduction
Categorical bins Introduction
tf.feature_column.bucketized_column Introduction
"Bridges" in Python coding Introduction
Make selected text bold Introduction
Open the page of the downloaded list of webpage browser Introduction
Calculation in an Excel Sheet, Style, Bold, and Color Introduction
Bring/activate an application/window to most front/foreground Introduction
Bind/link multiple commands to buttons Introduction
Bind Python functions and methods to events (similar to if loops) Introduction
Turn on and off with mouse press or a process/button switch
FLAT, RAISED, SUNKEN, GROOVE and RIDGE in Tkinter button relief styles Introduction
Bag-of-words model Introduction
Find the best word/text similarity Introduction
(Single and multiple enter/input) box for pop-up window Introduction
Calibrate and put a scale bar, and draw a line segment on an image Introduction
Merge/combine two text files into a new text file, add a new line to the beginning of a text file Introduction
Build databases with different/uncertain number of members Introduction
Wheatstone bridge and its simulation Introduction. code
Open webpages in internet browsers (Chrome, Microsoft edge, IE browser) 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.
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

Create images with global, adaptive mean, adaptive Gaussian, binary, trunc, Tozero, and tozero thresholds.

Bail out/terminate of a loop code
Find/search birthyear by name code
Dropdown box/option/selection/choice Introduction
Check if both files are the same file, e.g. symbolic link, shortcut Introduction
Invert the contrasts of black and white images (Code)
watchdog with conditioning break Introduction
Top (ranking, best, must know) Python libraries/modules Introduction
Measure length/distance on an image w/o calibrated bar Introduction
Break/exit/skip a function/code line after a certain time Introduction
Binary classifiers Introduction
Generate text file with the bank of collecting all words, characters and strings from news Introduction
Comparison between Python, Blue Prism, UiPath, Automation Anywhere Introduction
Binary images Introduction
Wafer bin map (WBM) Introduction
Detection procedures/processes of spatial defect patterns (bins) in wafers Introduction
Smooth images (make image blurry) Introduction