DevOps & CI/CD Tools with Examples
DevOps & CI/CD Tools Overview with Examples
1. GitHub Actions
GitHub Actions automates workflows directly in your GitHub repository for CI/CD, testing, and deployment.
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ree image-to-video generator tools
Here’s a clean table with free image-to-video generator tools and their official links 👇 # Tool Name Link 1 Media.io Image to Video AI https://www.media.io/image-to-video-ai.html 2 Toolplay Free Image to Video Generator https://toolplay.ai/tools/free-ai-image-to-video-generator 3 Imgkits Image to Video AI https://www.imgkits.com/image-to-video 4 Akool Image-to-Video AI https://akool.com/apps/image-to-video 5 VeeGen AI – Image to Video https://veegen.ai
ml
Machine Learning Concepts Machine Learning (ML) Concepts with Code Examples 1. Supervised Learning Supervised learning involves training a model on labeled data to make predictions. Common tasks: classification, regression. Example: Classification using Scikit-learn from sklearn.datasets import load_iris from sklearn.ensemble import RandomForestClassifier data = load_iris() X, y = data.data, data.target model = RandomForestClassifier() model.fit(X, y) print(model.predict([[5.1, 3.5, 1.4, 0.2]])) 2. Unsupervised Learning Unsupervised learning finds hidden patterns in data without labeled outcomes. Common tasks: clustering, dimensionality reduction. Example: K-Means Clustering from sklearn.cluster import KMeans from sklearn.datasets import load_iris X = load_iris().data kmeans = KMeans(n_clusters=3, random_state=0) kmeans.fit(X) print(kmeans.labels_[:10]) ...
Example TensorFlow
1. TensorFlow – Image Classification (MNIST Digits) python Copy Edit import tensorflow as tf from tensorflow.keras.datasets import mnist from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Flatten # Load and preprocess data (x_train, y_train), (x_test, y_test) = mnist.load_data() x_train, x_test = x_train / 255.0 , x_test / 255.0 # Build model model = Sequential([ Flatten(input_shape=( 28 , 28 )), Dense( 128 , activation= 'relu' ), Dense( 10 , activation= 'softmax' ) ]) # Compile and train model. compile (optimizer= 'adam' , loss= 'sparse_categorical_crossentropy' , metrics=[ 'accuracy' ]) model.fit(x_train, y_train, epochs= 5 ) # Evaluate model.evaluate(x_test, y_test) 2. PyTorch – Sentiment Analysis with LSTM python Copy Edit import torch import torch.nn as nn from torchtext.legacy.datasets import IMDB from torchtext.legacy.data import Field, BucketIterator # Defin...
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