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

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Clusters (Kubernetes, Apache Mesos, Spark Standalone, Apache Hadoop YARN) in Apache Spark Introduction
Using proxy labels, building a labeling system, and utilizing a labeling service when historical labeled data is unavailable for ML projects Introduction
Google Cloud Platform (GCP) versus Apache Hadoop Introduction
Comparison between RDBMS (Relational Database Management Systems) and Apache Hive Introduction
Apache HBase Introduction
Hive in Hadoop Introduction
HDFS (Hadoop Distributed File System) Introduction
Analyzing Data in Hadoop (HDFS, YARN, Apache Hive, Pig, HBase, Spark) Introduction
Data Storage in Hadoop (HDFS, HBase and YARN) Introduction
Data storage in Hadoop Introduction
Ingesting data in Hadoop (Sqoop, Flume, Kafka, NiFi) Introduction
Hadoop MapReduce used by Google, Netflix, Amazon and Machine Learning Introduction
Hadoop MapReduce Introduction
Apache hadoop and hadoop ecosystem Introduction
Hash function and hash value Introduction
Handwritten digit recognition Introduction
Biological (human brain) and AI neural networks Introduction
Computer hardware architecture Introduction
Soft constraints and hard constraints Introduction
Hill Climbing Introduction
Hidden Markov Model (HMM) Introduction
Hidden state Introduction
Informed search algorithms/heuristic search algorithms Introduction
Finite-horizon MDP (Markov Decision Process) Introduction
Example of building robot (self-driving) systems with automated ML: helicopter Introduction
Hyperbolic tangent (Tanh) function Introduction
Comparison among sigmoid, hyperbolic tangent (tanh) and rectified linear unit (ReLU) functions Introduction
Holidays/Festivals/Vocations (Thanksgiving/Christmas) Introduction
Generalization Error/Generalization Loss/Test Error/Expected Error of Hypothesis/Risk Introduction
Hyperparameter tuning (model tuning) Introduction
Updating Hypothesis (ĥ) and/or Parameter θ^ in ML Introduction
Standard hold-out validation Introduction
Soft margin versus hard margin in ML Introduction
Comparison among classifier, hyperplane and decision boundary Introduction
Hidden layer in deep learning neural network Introduction
Hyperplane/Decision Boundary in ML Introduction
Hide/turn on/off axes/axis on matplotlib Introduction
Plot horizontal stacked bar/histogram Introduction
Machine learning example step-by-step (prediction of house price) Introduction
One hot encoding Introduction
Locate/find the center/coordinates of a bright (maximum/highest intensity) spot in an image Introduction
Hook Introduction
tf.keras.layers.Hashing Introduction
Create CSV files (e.g. with headers only) Introduction
   
Heatmap Introduction
◆ Heatmap with input from a csv/pkl file Introduction
◆ go.Figure (Code). (Code)
◆ go.Heatmap (Code)
◆ Generating heatmaps for grouped data occurrences Introduction
◆ Plot a heatmap with three columns of data Introduction
◆ plotly.graph_objects Introduction
   
Histogram  
◆ Plot pixel intensities (histogram) along a line of an image Introduction
◆ Plot histogram Introduction
◆ Histogram for wafer analysis (e.g. percentage and frequency of grey level in the image) Introduction
   
Match on images to find and to highlight unsimilar (threshold=0) to identical (threshold=1) regions of an image that match a template with a cross-correlation method code
Launch help menu Introduction
Table of powerPoint shortcut hotkeys Introduction
Table of PC/computer/Windows shortcut hotkeys Introduction
Table of Word shortcut hotkeys Introduction
Table of Excel shortcut hotkeys Introduction
Table of Chrome shortcut hotkeys Introduction
Table of digital micrograph (DM) shortcut hotkeys Introduction
Send emails in HTML and text formats Introduction
Highlight texts or make selection Introduction
Handle NaN value in DataFrame, replace empty cells with ... Introduction
Handle "No Results", "Not Found" (Error vs. Exceptions) Introduction
top and left, width and height for pptx (e.g. align the top-left corner of the image to the center of the slide no matter how the size of the images changes) Introduction
Hidden Markov Models (HMMs) Introduction
Convert DataFrame to a HTML Table and save as a HTML webpage Introduction
Count how many (number) files and folders in a directory Introduction
Deviation Probability (Hoeffding Bound) Introduction
if not hasattr/if hasattr (attribute) Introduction
Human inspection of defects in wafer map Introduction
Hoeffding Inequality Introduction
Confusion matrix heatmap Introduction
Comparison between machine learning and human beings Introduction
Modify HTML webpage Introduction
Modify HTML webpage (e.g. with graph network by adding/inserting text/hyperlink in) Introduction
Get header/column names from DataFrame Introduction
hotkey('h') in pyautogui Introduction
hyperspy application in STEM, EDS, and EELS analysis Introduction
Compute the similarity between two text documents/files (with heatmap) Introduction
Check if all the (and how many, length of a string) characters in the text are digits/numbers Introduction
Plot graph/figure/image from CSV file/DataFrame by removing/hiding blank/empty cells with axis range (plt.xlim()) Introduction
Plot multiple images on the same figure by hiding x- and y-labels on axis Introduction
Change/rename a column name/header in a CSV file Introduction
History/hot topics of machine learning Introduction
Delete the column/row in a CSV file if they are empty or less than a number (or header/index only) Introduction
Multiple headers in a csv file: Count the number of header rows first and then split a single csv file to multiple csv files Introduction
   
Hypothesis (predicted output (h(x))) Introduction
◆ Finite Hypothesis Class versus Infinite Hypothesis Class Introduction
  ✔ Finite Hypothesis Class/finite Hypothesis Analysis Introduction
  ✔ Infinite Hypothesis Class Introduction
◆ Hypothesis space/model space/search space Introduction
◆ "Model"versus "hypothesis" in ML Introduction
◆ Hypothesis class/hypothesis family/predictor class/model class/hypothesis family/predictor family/model family (h) 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
Output the row into dataframe if the value of the cell in a column contains a specific substring in a csv file (with headers) Introduction
Hide x-axis tick labels (only show some labels) where x values are under certain conditions Introduction
Highlight the plotted dots uncer certain condition Introduction
Only use the first 4 characters in the headers of the table for pptx/dataframe Introduction
Embed/hide codes or markers into HTML files Introduction
Cheatsheet about headers (column names) in DataFrame Introduction
HTTP(Hypertext Transfer Protocol)/URL (Uniform Resource Locator) Introduction

 

 

   
header= CSV: Whether to write out the column names (default True). Introduction. (code). Row number(s) to use as the column names, and the start of the data. Defaults to 0 if no names passed, otherwise None. Explicitly pass header=0 to be able to replace existing names. The header can be a list of integers that specify row locations for a multi-index on the columns E.g. [0,1,3]. Intervening rows that are
not specified will be skipped (e.g. 2 in this example are skipped). Note that this parameter ignores commented lines and empty lines if skip_blank_lines=True (the default), so header=0 denotes the first line of data rather than the first line of the file.
head() (code)
.hold() Holds a key conveniently and passes a string from the pyautogui.KEYBOARD_KEYS such as shift, ctrl, alt. Introduction
hotkey() Several key strings which will be pressed down in order, and then released in reverse order. Strings can be passed to this function: '\t', '\n', '\r', ' ', '!', '"', '#', '$', '%', '&', "'", '(', ')', '*', '+', ',', '-', '.', '/', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', ':', ';', '<', '=', '>', '?', '@', '[', '\\', ']', '^', '_', '`', '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', '{', '|', '}', '~', 'accept', 'add', 'alt', 'altleft', 'altright', 'apps', 'backspace', 'browserback', 'browserfavorites', 'browserforward', 'browserhome', 'browserrefresh', 'browsersearch', 'browserstop', 'capslock', 'clear', 'convert', 'ctrl', 'ctrlleft', 'ctrlright', 'decimal', 'del', 'delete', 'divide', 'down', 'end', 'enter', 'esc', 'escape', 'execute', 'f1', 'f10', 'f11', 'f12', 'f13', 'f14', 'f15', 'f16', 'f17', 'f18', 'f19', 'f2', 'f20', 'f21', 'f22', 'f23', 'f24', 'f3', 'f4', 'f5', 'f6', 'f7', 'f8', 'f9', 'final', 'fn', 'hanguel', 'hangul', 'hanja', 'help', 'home', 'insert', 'junja', 'kana', 'kanji', 'launchapp1', 'launchapp2', 'launchmail', 'launchmediaselect', 'left', 'modechange', 'multiply', 'nexttrack', 'nonconvert', 'num0', 'num1', 'num2', 'num3', 'num4', 'num5', 'num6', 'num7', 'num8', 'num9', 'numlock', 'pagedown', 'pageup', 'pause', 'pgdn', 'pgup', 'playpause', 'prevtrack', 'print', 'printscreen', 'prntscrn', 'prtsc', 'prtscr', 'return', 'right', 'scrolllock', 'select', 'separator', 'shift', 'shiftleft', 'shiftright', 'sleep', 'space', 'stop', 'subtract', 'tab', 'up', 'volumedown', 'volumemute', 'volumeup', 'win', 'winleft', 'winright', 'yen', 'command', 'option', 'optionleft', 'optionright'. (code)
header CSV: skip header. code. code. code. code.
height code, code. code, (code), (code)
year, month, date, hour, minute, and second "datefmt='%Y-%m-%d %H:%M:%S')": year, month, date, hour, minute, and second. Instruction.
hex() Convert an integer number (of any size) to a lowercase hexadecimal string prefixed with “0x”
scipy.linalg.hadamard Create an Hadamard matrix.
scipy.linalg.hankel Create a Hankel matrix.
scipy.linalg.helmert Create a Helmert matrix.
scipy.linalg.hilbert Create a Hilbert matrix.
Hebel Is a tool for deep learning with neural networks using GPU acceleration with CUDA through pyCUDA. Right now, Hebel implements feed-forward neural networks for classification and regression on one or multiple tasks. Other models such as Autoencoder, Convolutional neural nets, and Restricted Boltzman machines are planned for the future.
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.
Get dimensions (sizes) of image:
dimensions = img.shape
Get height, width, number of channels in image
height = img.shape[0]
width = img.shape[1]
channels = img.shape[2]		 Introduction. General, code. code. code. code. code. code.
hist() (code)
.hstack General, code.
header (=None) Replace/change to new headers in a csv file; read (pandas) csv file with no header. CSV: Introduction
t.hideturtle()/t.ht() Make the turtle invisible while you’re in the middle of doing some complex drawing, because hiding the turtle speeds up the drawing observably. (code)
.height (code)
highlightbackground= (code)
highlightthickness= (code)
driver.window_handles <Instruction>
driver.current_window_handle <Instruction>
slide_height (code).
fig.write_html() (code).

 

 

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