Deep Learning Keras Tf Tutorial

Deep Learning Keras Tf Tutorial - learn deep… | Awesome Repos

Features

Deep Learning Training Toolsets - Provides a comprehensive toolkit for building and training deep learning models from scratch.
Gradient-Based Parameter Updates - Updates model parameters iteratively using gradient-based backpropagation to minimize loss.
Loss Function Implementations - Calculates training loss to guide the optimization process during neural network training.
Modular Layer Compositions - Constructs neural networks by stacking modular functional layers to transform input data.
Keras Model Implementations - Offers a structured educational guide for implementing deep learning models using the Keras API.
Stochastic Gradient Descent - Optimizes model weights using stochastic gradient descent to navigate the loss landscape.
Deep Learning Tutorials - Serves as an educational resource for building and training neural networks using high-level deep learning frameworks.
Machine Learning Tutorials - Provides a comprehensive curriculum of tutorials for constructing and training predictive models through hands-on exercises.
Class Imbalance Handling - Mitigates data bias by adjusting training weights to handle class imbalances in datasets.
Data Input Pipelines - Optimizes data input pipelines to ensure efficient processing and prevent idle hardware time.
Data Prefetching Pipelines - Implements background data prefetching to keep hardware accelerators saturated during model training.
Model Quantization Tools - Reduces model precision to decrease memory usage and accelerate inference on resource-constrained hardware.
Machine Learning Optimization - Provides techniques for optimizing neural network performance, including weight quantization and data balancing strategies.
Computer Vision Modelings - Provides modular components for computer vision tasks including data augmentation to reduce overfitting.
Natural Language Processing - Covers natural language processing tasks including transformer model construction and word embedding generation.
Word Embeddings - Learns dense vector representations of words to capture semantic relationships for NLP tasks.
Weight Quantization - Compresses model weights through quantization to reduce memory footprint and accelerate inference.
Self-Attention Mechanisms - Utilizes self-attention mechanisms to weigh sequence importance for improved prediction accuracy.
Text Classifiers - Categorizes text sequences using recurrent neural networks to learn patterns from training data.
Image Augmentations - Improves model generalization by applying random image transformations like rotation and flipping.
Transformer Language Models - Constructs transformer-based language models for natural language processing tasks.
Computational Graphs - Defines and executes neural network models using compiled computational graphs for high-performance processing.
Training Metric Monitors - Tracks training progress and loss metrics to evaluate model accuracy during the learning process.

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codebasicsdeep-learning-keras-tf-tutorial

Features

Open-source alternatives to Deep Learning Keras Tf Tutorial

snowkylin/tensorflow-handbook

czy36mengfei/tensorflow2_tutorials_chinese

bojone/bert4keras

graykode/nlp-tutorial

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Open-source alternatives to Deep Learning Keras Tf Tutorial

snowkylin/tensorflow-handbook

czy36mengfei/tensorflow2_tutorials_chinese

bojone/bert4keras

graykode/nlp-tutorial