mrdbourke/tensorflow-deep-learning

Features

• Deep Learning Courses - Delivers a comprehensive deep learning course teaching neural network construction using TensorFlow 2.x.
• Jupyter Notebook Curricula - Delivers a structured deep learning curriculum as Jupyter notebooks with embedded code and exercises.
• Convolutional Neural Network Training - Trains models to recognize and classify objects in images using convolutional and pooling layers.
• Image Classification - Builds convolutional neural networks to recognize and classify objects in images using TensorFlow 2.x.
• Neural Network Classification - Trains models to assign categorical labels to data points using activation functions and loss functions.
• Transfer Learning Pipelines - Reuses pre-trained model weights as feature extractors or fine-tunes later layers for new tasks.
• Natural Language Processing - Trains models to classify and understand text data using embeddings and recurrent or transformer layers.
• Neural Network Construction - Walks through constructing, training, and evaluating neural networks for regression, classification, and image recognition.
• Regression Neural Networks - Trains models to predict continuous numerical values from input features using dense layers and optimizers.
• Sequential-First Pedagogies - Teaches neural network construction by starting with the Sequential API before introducing other approaches.
• Neural Network Training - Provides walkthroughs for constructing, training, and evaluating neural network models.
• Pre-training Transfer Learning - Adapts pre-trained neural networks to new tasks through feature extraction and fine-tuning.
• Time Series Forecasting - Trains models to predict future values from sequential data using past observations and trends.
• Best Weight Checkpointers - Saves the best model weights during training for resumption and evaluation without retraining.
• Transfer Learning - Reuses features from a pre-trained model and adapts them to a new task with less training data.
• TensorFlow Developer Certificate Preparation - Provides study materials and practice exercises aligned with the official TensorFlow Developer Certificate exam.
• Certification Preparation Materials - Provides study materials and practice exercises aligned with the official TensorFlow Developer Certificate exam.
• Classification Training - Builds and trains neural networks to classify images using TensorFlow 2.x.
• Mixed Precision Training - Trains models faster and uses less memory by combining 16-bit and 32-bit floating-point arithmetic.
• Transfer Learning Guides - Provides guides on reusing pre-trained model features and fine-tuning them for new tasks.
• Model Checkpointing - Saves the best model weights during training and reloads them for evaluation or further training.
• Text Tokenization - Tokenizes text data to classify content or predict sequences using a neural network.
• Layer Unfreezing Fine-Tuners - Unfreezes and retrains later layers of a pre-trained model to adapt it to a new, similar task.
• Feature Extraction Pipelines - Uses a pre-trained model as a fixed feature extractor to train a new classifier on a smaller dataset.
• TensorFlow Time Series - Provides instructions for training models to predict future values from sequential data using TensorFlow.
• Model Export Submissions - Exports trained models in .h5 format for direct submission to the TensorFlow Developer Certificate exam.
• Educational Resources - Comprehensive tutorial series for learning deep learning with TensorFlow.

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