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| 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 |
Introduction |
| ◆ |
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 ...
|
Introduction |
Find minimum and maximum values in a list
|
Introduction |
| 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
|
Introduction |
| 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
|
Introduction |
| 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
|
Introduction |
| 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
|
Introduction |
| 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 |
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