Mastering Neural Networks: 100 Quick Python One-liner Codes for Building Artificial Neural Network Models

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Building neural networks is fundamental to deep learning, allowing machines to learn complex patterns and make predictions across various tasks. This list presents concise code snippets covering key aspects of neural network development using TensorFlow and Keras. From basic sequential models for regression and classification to more advanced techniques like convolutional and recurrent layers, these one-liners provide quick references for constructing, training, and evaluating neural networks. The list also includes tips for fine-tuning, using pre-trained models, implementing custom training loops, and integrating popular NLP models like BERT and GPT. Whether you’re a beginner or an experienced practitioner, these snippets serve as a handy guide for navigating the neural network landscape in machine learning and deep learning applications.

TensorFlow Sequential Model

1. Initialize TensorFlow Sequential Model: model = tf.keras.Sequential()
2. Add Dense Layer: model.add(Dense(units=64, input_dim=100))
3. Add Activation Function: model.add(Activation('relu'))
4. Add Output Layer: model.add(Dense(units=1, activation='sigmoid'))