https://github.com/aqueeqazam/smart-guidance-of-deep-learning-with-tensorflow-and-keras-apis

By leveraging smart guidance techniques and tools, developers can build and train deep learning models more efficiently, effectively, and accurately, leading to wider adoption and more innovative applications of deep learning technology.
https://github.com/aqueeqazam/smart-guidance-of-deep-learning-with-tensorflow-and-keras-apis

artificial-neural-networks deep-learning keras neural-network tenosrflow

Last synced: 17 days ago
JSON representation

By leveraging smart guidance techniques and tools, developers can build and train deep learning models more efficiently, effectively, and accurately, leading to wider adoption and more innovative applications of deep learning technology.

• Host: GitHub
• URL: https://github.com/aqueeqazam/smart-guidance-of-deep-learning-with-tensorflow-and-keras-apis
• Owner: AqueeqAzam
• Created: 2024-07-18T16:57:12.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-07-24T11:27:33.000Z (6 months ago)
• Last Synced: 2024-11-14T16:43:14.101Z (3 months ago)
• Topics: artificial-neural-networks, deep-learning, keras, neural-network, tenosrflow
• Language: Jupyter Notebook
• Homepage:
• Size: 60.5 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Smart Guidance of Deep Learning with TensorFlow and Keras APIs

Deep learning has revolutionized the field of artificial intelligence, enabling machines to learn and improve from experience. However, building and training deep learning models can be a complex and time-consuming process, requiring expertise in both the underlying algorithms and the programming frameworks used to implement them.

TensorFlow and Keras are two popular open-source APIs used for building and training deep learning models. TensorFlow provides a low-level API for building and training models, while Keras provides a high-level API for building and training models, making it easier to use for beginners.

Smart Guidance refers to the use of advanced techniques and tools to guide the development and training of deep learning models, making the process more efficient, effective, and accessible to a wider range of users.

# Key Features:

Automated Model Selection: Automatically select the best deep learning model for a given problem.

Hyperparameter Tuning: Automatically tune hyperparameters for optimal model performance.

Model Pruning: Automatically remove unnecessary model components for improved efficiency.

Transfer Learning: Leverage pre-trained models for faster training and improved performance.

Real-time Feedback: Provide real-time feedback and guidance during the model development and training process.

# Benefits:

Improved Efficiency: Reduce the time and effort required to build and train deep learning models.

Improved Accuracy: Achieve better model accuracy and performance through automated hyperparameter tuning and model selection.

Increased Accessibility: Make deep learning more accessible to a wider range of users, including those without extensive expertise in deep learning algorithms and programming frameworks.

# Applications:

Computer Vision: Image classification, object detection, segmentation, and generation.

Natural Language Processing: Text classification, language translation, and language generation.

Speech Recognition: Speech-to-text and voice recognition.

By leveraging smart guidance techniques and tools, developers can build and train deep learning models more efficiently, effectively, and accurately, leading to wider adoption and more innovative applications of deep learning technology.