ai sites

 

General AI Tools & Platforms

  1. OpenAI ChatGPT — https://chat.openai.com

  2. Bard (Google AI) — https://bard.google.com

  3. Claude (Anthropic) — https://www.anthropic.com/claude

🎨 AI Image & Art Generators

  1. Stable Diffusion — https://stability.ai

  2. Midjourney — https://midjourney.com

  3. DALL·E 3 (OpenAI) — https://openai.com/dall-e-3

📸 Safe Photo & Video Tools

  1. Runway ML — https://runwayml.com

  2. Canva AI Image Generator — https://www.canva.com/features/ai-image-generator

🤖 AI Coding & Dev Tools

  1. GitHub Copilot — https://github.com/features/copilot

  2. Tabnine — https://www.tabnine.com

  3. Hugging Face — https://huggingface.co

📚 AI Learning Resources

  1. Fast.ai — https://www.fast.ai

  2. Coursera AI Courses — https://www.coursera.org/browse/data-science/ai

  3. deeplearning.ai — https://www.deeplearning.ai

Comments

Post a Comment

Popular posts from this blog

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
Read more

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]) ...
Read more

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...
Read more