Example TensorFlow

 

1. TensorFlow – Image Classification (MNIST Digits)

python
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)

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2. PyTorch – Sentiment Analysis with LSTM

python
import torch
import torch.nn as nn
from torchtext.legacy.datasets import IMDB
from torchtext.legacy.data import Field, BucketIterator

# Define fields
TEXT = Field(tokenize='spacy', include_lengths=True)
LABEL = Field(sequential=False, dtype=torch.float)

# Load dataset
train_data, test_data = IMDB.splits(TEXT, LABEL)

# Build vocab
TEXT.build_vocab(train_data, max_size=10000)
LABEL.build_vocab(train_data)

# Create iterators
train_iter, test_iter = BucketIterator.splits((train_data, test_data), batch_size=64, sort_within_batch=True)

# LSTM model
class LSTMModel(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim):
        super(LSTMModel, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.lstm = nn.LSTM(embedding_dim, hidden_dim)
        self.fc = nn.Linear(hidden_dim, 1)
        
    def forward(self, text, text_lengths):
        embedded = self.embedding(text)
        packed = nn.utils.rnn.pack_padded_sequence(embedded, text_lengths)
        output, (hidden, _) = self.lstm(packed)
        return torch.sigmoid(self.fc(hidden[-1]))

# Initialize and train model (training loop not shown for brevity)

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3. Hugging Face Transformers – Text Summarization

python
from transformers import pipeline

# Load summarization pipeline
summarizer = pipeline("summarization")

# Input text
text = """
The Eiffel Tower is one of the most iconic structures in the world, located in Paris, France.
It was completed in 1889 and stands at 324 meters tall.
Millions of tourists visit it every year to enjoy the view and its architectural beauty.
"""

# Generate summary
summary = summarizer(text, max_length=50, min_length=20, do_sample=False)
print(summary[0]['summary_text'])

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4. Keras – Digit Recognition with MNIST

python
from keras.models import Sequential
from keras.layers import Dense, Flatten
from keras.datasets import mnist
from keras.utils import to_categorical

# Load data
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
y_train, y_test = to_categorical(y_train), to_categorical(y_test)

# 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='categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=5)

# Evaluate
model.evaluate(x_test, y_test)

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5. OpenAI GPT API – Chatbot Response (Python & API Key Required)

python
import openai

openai.api_key = "your-api-key"

response = openai.ChatCompletion.create(
    model="gpt-4",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is the capital of France?"}
    ]
)

print(response['choices'][0]['message']['content'])

🔐 Note: Replace "your-api-key" with your actual OpenAI API key from https://platform.openai.com.

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Let me know if you want any of these converted into notebooks or expanded tutorials!