Complete Python Topics for Data Analysis: https://t.me/sqlspecialist/548
Deep Learning Basics with TensorFlow:
Deep Learning is a subset of machine learning that involves neural networks with multiple layers (deep neural networks). TensorFlow is an open-source deep learning library developed by Google.
1. Introduction to Neural Networks:
- Perceptrons and Activation Functions:
- Basic building blocks of neural networks.
import tensorflow as tf
# Create a simple perceptron
perceptron = tf.keras.layers.Dense(units=1, activation='sigmoid', input_shape=(input_size,))
- Activation Functions:
- Functions like ReLU or sigmoid introduce non-linearity.
activation_relu = tf.keras.layers.Activation('relu')
activation_sigmoid = tf.keras.layers.Activation('sigmoid')
2. Building Neural Networks:
- Sequential Model:
- A linear stack of layers.
model = tf.keras.Sequential([
tf.keras.layers.Dense(64, activation='relu', input_shape=(input_size,)),
tf.keras.layers.Dense(1, activation='sigmoid')
])
- Compiling the Model:
- Specify optimizer, loss function, and metrics.
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
3. Training Neural Networks:
- Fit Method:
- Train the model on training data.
model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_val, y_val))
- Model Evaluation:
- Assess the model's performance on test data.
test_loss, test_accuracy = model.evaluate(X_test, y_test)
4. Convolutional Neural Networks (CNNs):
- Convolutional Layers:
- Specialized layers for image data.
model.add(tf.keras.layers.Conv2D(filters=64, kernel_size=(3, 3), activation='relu', input_shape=(height, width, channels)))
- Pooling Layers:
- Reduce dimensionality.
model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))
5. Recurrent Neural Networks (RNNs):
- LSTM Layers:
- Handle sequences of data.
model.add(tf.keras.layers.LSTM(units=50, return_sequences=True, input_shape=(timesteps, features)))
- Embedding Layers:
- Convert words to vectors in natural language processing.
model.add(tf.keras.layers.Embedding(input_dim=vocab_size, output_dim=embedding_dim, input_length=max_length))
Deep learning with TensorFlow is powerful for handling complex tasks like image recognition and sequence processing.
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