🧩 Deep Learning Frameworks: TensorFlow & PyTorch

Deep Learning models को scratch से implement करना बहुत complex और time-consuming होता है। इस समस्या को solve करने के लिए TensorFlow और PyTorch जैसे frameworks बनाए गए हैं। ये high-level APIs provide करते हैं जिससे developers और researchers आसानी से models बना, train और deploy कर सकते हैं।

💻 Example (Keras API):

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

# Model definition
model = Sequential([
    Dense(64, activation='relu', input_shape=(10,)),
    Dense(32, activation='relu'),
    Dense(1, activation='sigmoid')
])

# Compile
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
    

💻 Example (PyTorch):

import torch
import torch.nn as nn
import torch.optim as optim

# Model definition
class SimpleNN(nn.Module):
    def __init__(self):
        super(SimpleNN, self).__init__()
        self.fc1 = nn.Linear(10, 64)
        self.fc2 = nn.Linear(64, 32)
        self.fc3 = nn.Linear(32, 1)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = torch.sigmoid(self.fc3(x))
        return x

# Initialize model
model = SimpleNN()
criterion = nn.BCELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)