Building Deep Learning Applications with Keras
1h 50mIntermediate2024-03-12
Authors
Isil Berkun
Data Scientist at Intel Corp.
Course details
Keras, a high-level neural networks API, is gaining popularity for its ease of use and versatility. With businesses rapidly moving towards AI solutions, you need an understanding of this valuable tool. In this course, data scientist Isil Berkun introduces you to Keras, highlights integrating it with TensorFlow and Theano backends, and offers practical insights into creating neural networks. Learn how to set up Keras, create neural networks, and use pre-trained models. Find out how to deploy models on platforms like Google Cloud. When you complete this course, you will be able to understand the Keras architecture, design and train deep learning models, and utilize them for real-world applications.
Skills covered
KerasTensorFlowTelecommunicationsNeural Networks and Deep LearningFull-Stack Web DevelopmentGoogleArtificial Intelligence (AI)Web DevelopmentNetwork and System AdministrationOpen SourceOne-Off
Concepts
0. Introduction
- 01 - Reshaping the world with deep learning
- 02 - Essential background and knowledge
- 03 - How to use Codespaces and the exercise files
1. Understanding Keras
- 04 - Understanding deep learning and Keras
- 05 - Neuron as we know it
- 06 - Exploring the TensorFlow and Theano backends
- 07 - Distinction between Keras and TensorFlow
2. Setting up Keras
- 08 - Keras installation with a TensorFlow backend on Windows
3. Getting Started with Keras Models
- 09 - The Train-Test-Evaluate cycle
- 10 - Introduction to the Keras Sequential API
- 11 - Data pre-processing for training
- 12 - Building a model using the Sequential API
4. Model Training and Performance Analysis
- 13 - Training models
- 14 - Model predictions and evaluation
- 15 - Visualize results and save the model
5. Leveraging Pre-Trained Models in Keras
- 16 - Exploring pre-trained models
- 17 - Image recognition with the ResNet50 model
6. Tools for Visualization and Assessment
- 18 - Exporting logs for Keras to TensorFlow
- 19 - Monitoring training performance with TensorBoard
- 20 - Visualizing computation graphs with TensorBoard
Conclusion
- 21 - Next steps
Related courses
- Building Deep Learning Applications with Keras 2.0 (2017)
- Recurrent Neural Networks
- Deep Learning and Generative AI: Data Prep, Analysis, and Visualization with Python
- Deep Learning with Python: Hands-On Introduction to Deep Learning Models
- Building and Deploying Deep Learning Applications with TensorFlow
- Building Computer Vision Applications with Python
- Deep Learning Fundamentals for Healthcare
- Deep Learning with Python: Convolutional Neural Networks
Related learn paths
- Fundamentals to Become a Machine Learning Engineer
- Advance Your Skills in Deep Learning and Neural Networks
- Advance Your Skills in Natural Language Processing
- Getting Started with AI and Machine Learning
- Explore AI for Data Engineering
- Moving from Data Scientist to Data Analyst
- Applying AI as a Tech Leader
- Advance Your Skills in AI and Machine Learning