🧠 The Deep Learning Pipeline

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1. Data Preparation & Augmentation

  • Build efficient input pipelines
  • Process unstructured data
  • Apply data augmentation

2. Model Architecture & Design

  • Choose appropriate layers
  • Select activation functions
  • Utilize transfer learning

3. Model Compilation

  • Select an optimizer (Adam, SGD)
  • Define a loss function
  • Choose evaluation metrics

4. Model Training

  • Run the training loop
  • Manage batches and epochs
  • Use callbacks

5. Evaluation & Analysis

  • Evaluate on test data
  • Analyze predictions and errors
  • Visualize with TensorBoard

6. Fine-Tuning & Transfer Learning

  • Freeze layers of pre-trained models
  • Implement learning rate schedules
  • Adapt general models

7. Inference & Deployment

  • Save the final model
  • Optimize for production
  • Export to a serving format

⏪ Back to Classical ML

  • Return to the classical machine learning pipeline for structured data.
🌳 Step 1: Data Preparation & Augmentation
1. DATA PREPARATION & AUGMENTATION │ ├── Data Loading & Pipelining │ │ ├── tf.data.Dataset ✓ Creating efficient, high-performance input pipelines in TensorFlow │ │ ├── torch.utils.data.DataLoader ✓ The standard data loader for batching and sampling in PyTorch │ │ └── Hugging Face datasets ✓ Library for easy access and processing of huge datasets │ ├── Image Data Augmentation │ │ ├── tf.keras.layers.RandomFlip ✓ Applies random horizontal/vertical flips │ │ ├── tf.keras.layers.RandomRotation ✓ Applies random rotation to images │ │ └── Albumentations ✓ A fast and flexible library for a wide variety of image augmentations │ └── Text Data Processing │ ├── Tokenization Hugging Face Tokenizers ✓ State-of-the-art tokenization (BPE, WordPiece) for NLP models │ └── Vectorization tf.keras.layers.TextVectorization ✓ Converts text tokens into numerical integer vectors
🌳 Step 2: Model Architecture & Design
2. MODEL ARCHITECTURE & DESIGN │ ├── Foundational Layers │ │ ├── tf.keras.layers.Dense ✓ Standard fully connected layer │ │ ├── tf.keras.layers.Conv2D ✓ Core layer for Convolutional Neural Networks (CNNs) for image processing │ │ ├── tf.keras.layers.LSTM / GRU ✓ Key layers for Recurrent Neural Networks (RNNs) for sequential data │ │ └── tf.keras.layers.MultiHeadAttention ✓ The core mechanism behind Transformer models │ ├── Activation Functions │ │ ├── tf.nn.relu ✓ Most common activation for hidden layers to introduce non-linearity │ │ ├── tf.nn.sigmoid ✓ Used for binary classification output layers to get a probability │ │ └── tf.nn.softmax ✓ Used for multi-class classification output layers │ └── Pre-trained Models (Transfer Learning) │ ├── TensorFlow Hub / Keras Applications ✓ Access to models like ResNet, MobileNet, BERT │ └── Hugging Face Hub ✓ The central repository for thousands of pre-trained models
🌳 Step 3: Model Compilation
3. MODEL COMPILATION │ ├── Optimizers │ │ ├── tf.keras.optimizers.Adam ✓ Robust, adaptive learning rate optimizer; a go-to default │ │ └── tf.keras.optimizers.SGD ✓ Stochastic Gradient Descent, often used with momentum │ └── Loss Functions │ ├── tf.keras.losses.BinaryCrossentropy ✓ For binary (two-class) classification problems │ ├── tf.keras.losses.CategoricalCrossentropy ✓ For multi-class classification (when labels are one-hot encoded) │ └── tf.keras.losses.MeanSquaredError ✓ Standard loss function for regression tasks
🌳 Step 4: Model Training
4. MODEL TRAINING │ ├── Core Training Loop │ │ └── model.fit() ✓ High-level Keras method to handle the entire training process over epochs and batches │ └── Callbacks │ ├── tf.keras.callbacks.ModelCheckpoint ✓ Automatically saving the best model during training │ ├── tf.keras.callbacks.EarlyStopping ✓ Stopping training when a monitored metric has stopped improving │ └── tf.keras.callbacks.ReduceLROnPlateau ✓ Reducing the learning rate when performance plateaus
🌳 Step 5: Evaluation & Analysis
5. EVALUATION & ANALYSIS │ ├── Performance Metrics │ │ └── model.evaluate() ✓ Calculating final loss and metrics on the unseen test dataset │ └── Model Understanding & Explainability │ ├── TensorBoard ✓ The standard suite for visualizing DL metrics, graphs, and profiling │ ├── Netron ✓ A viewer for neural network, deep learning, and machine learning models │ └── SHAP / LIME ✓ Libraries for explaining the output of any machine learning model
🌳 Step 6: Fine-Tuning & Transfer Learning
6. FINE-TUNING & TRANSFER LEARNING │ ├── Layer Management │ │ └── layer.trainable = False ✓ Freezing layers of the base model to prevent their weights from being updated │ └── Learning Rate Schedules │ ├── tf.keras.optimizers.schedules.ExponentialDecay ✓ A common strategy to gradually lower the learning rate during training │ └── Hugging Face Transformers Trainer ✓ Includes advanced schedulers and a robust training loop for fine-tuning
🌳 Step 7: Inference & Deployment
7. INFERENCE & DEPLOYMENT │ ├── Model Saving & Exporting │ │ └── model.save() ✓ Saving the complete model (architecture, weights, optimizer state) to a single file │ └── Optimization for Production │ ├── Quantization TensorFlow Lite ✓ Reducing model precision (e.g., float32 to int8) to decrease size and latency │ ├── Pruning TensorFlow Model Optimization Toolkit ✓ Removing unnecessary weights from the network to make it smaller │ └── Format Conversion ONNX ✓ Open Neural Network Exchange format for model interoperability