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




TensorFlow Data Validation (TFDV) Introduction
Example of Vertex AutoML Vision Introduction
Identifying the business value of using ML Introduction
Hash function and hash value Introduction
Global variables Introduction
Computer vision Introduction
.values(): Return the dictionary's values.
Introduction. (code). General.
sys.version code.
sys.version_info code.
.value_counts(): value_counts(normalize=False, sort=True, ascending=False, bins=None, dropna=True).  (code) (code)
Default values in functions Introduction
Default value for list Introduction
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.
va='center' code.
.vstack General, code.
GetKeyState(VK_NUMLOCK): Turn off or on Num Lock. .press("numlock") (code).
GetKeyState(VK_CAPITAL) Caps Lock, .press("capslock"). (code).
hotkey('v') Introduction
StringVar() (code)
textvariable= (code)
.vocabulary_ (code)
vector_size=: The number of dimensions of the embeddings and the default is 100. (code). (code).
Viewport Introduction
values[] Introduction
vim Introduction
.DataFrame() Introduction
Examples of matplotlib (image/data) visualizations Introduction
Browser-based visualization tool Introduction
.norm() (Taxicab Norm, Manhattan Norm, Euclidian Norm and Vector Max Norm) Introduction
Common values in two pandas series Introduction
Categorical variables Introduction
Tensors and vectors Introduction
Word vectors Introduction
except ValueError Introduction
Dummy variables Introduction
Vertex AI
Sequential API to create a Keras model with TensorFlow (e.g. on Vertex AI platform) Introduction
Vertex AI Feature Store Introduction
tf.Variable Introduction
Feature and feature vector (extract features) Introduction
Find the values of the keys Introduction
Work (read, write, insert and delete rows and columns, and merge and unmerge cells, shift/move cell values) in Excel sheets Introduction
Calculate/pass the arbitrary (any) number of variables or input arguments Introduction
Access variable inside and outside a function Introduction
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
Handle NaN value in DataFrame, replace empty cells with ...
Find minimum and maximum values in a list
Show/open images in any image viewer code, code.

Change/convert a colored image to a grey image(, and then show pixel values).

cv2, cv2, cv2/skimage. cv2/skimage. PIL. matplotlib.
Count the number of lines (rows) and columns in a txt (and a csv) file, count different numbers in each region in a column, count missing or not available values CSV: Introduction. code.
Remove duplicate cell values from a csv file CSV: Introduction
Mean (average, .mean())/.sum()/maximum(.max())/minimum(.min())/number of non-null values(.count())/.median()/variance(.var())/standard deviation(.std()/pstdev()) Introduction
Get maximum and minimum value of column and its index Introduction
Get the csv/pandas cell value with certain condition Introduction
Change csv cell value under conditions Introduction
Check if CSV cell value is NaN Introduction
Global access to a variable inside a function from outside of the function
Change/swap values in a list Introduction
Variable length arguments (*args and **kwargs) Introduction
Libraries used to convert incident documents into numerical vectors Introduction
Support-vector machines(SVM)/support-vector networks(SVN) Introduction
Support-vector clustering (SVC) Introduction
Pixel values on specific pixel in an image Introduction
Pass variables between functions/from one to another Introduction
Methods of data and information visualization Introduction
VGG16 Introduction
Clustering in computer vision Introduction
Random variable Introduction
Save dynamic graph as a movie/video or split a movie to image frames Introduction
Optimal value function in Markov Decision Process (MDP) Introduction
Policy iteration versus value iteration Introduction
Value iteration Introduction
Latent features and latent variables Introduction
Vanishing gradients in ML Introduction
Variance of input data for ML Introduction
Vectorization Introduction
Linear correlation between two variables with Pearson Correlation Coefficient, Spearman Rank Correlation Coefficient, Kendall's Tau, Linear Regression, Coefficient of Determination and Correlation Ratio
Holidays/Festivals/Vocations (Thanksgiving/Christmas) Introduction
Comparisons among Manual Search, Vertex Vizier, AutoML and Early stopping on google cloud Introduction
Validation error Introduction
Standard hold-out validation Introduction
Leave-One-Out Cross-Validation (LOOCV) Introduction
K-Fold Cross-Validation Introduction
Cross-Validation in ML Introduction
Bias and variance, and bias-variance trade-off in ML Introduction
Support Vector Machines (SVM) and Logistic Regression Introduction
Feature vector and number of features Introduction
Transpose of vector and matrix Introduction
Check if a variable does exist/is assigned/defined Introduction
Check if a variable is a number or string Introduction
Create dictionary from nested (sublist) list and get the values with keys Introduction
y axis values are not ordered (disordered) Introduction
Download Youtube video Introduction
Calculation with combinations of variables from lists
Good research topics in the field of semiconductor manufacturing and computer vision Introduction
Autonomous vehicles/cars and machine learning Introduction
Variance Inflation Factors (VIFs) Introduction
Send a variable from one script (back) to another script with a function Introduction
Overfitting and underfitting Introduction
Validation Introduction
Train-dev-test split (training-validation-testing split: Ratio for splitting dataset into training, validation and test set
Virtual reality (VR), augmented reality (AR), and mixed reality (MR) Introduction
Word cloud visualization Introduction
Color and rotate/vertical text in pptx Introduction
Comparative overview of multivariate statistical methods (Correlation Analysis, Regression Analysis, Factor Analysis, Cluster Analysis, Principal Component Analysis (PCA), Canonical Correlation Analysis, Discriminant Analysis, Path Analysis, Structural Equation Modeling (SEM), Multivariate Analysis of Variance (MANOVA), Analysis of Covariance (ANCOVA) ): purposes, variables, and outputs Introduction