Deep Learning with TensorFlow: Insights and Innovations
3h 6mIntermediate2024-10-09
Authors
Isil Berkun
Data Scientist at Intel Corp.
Course details
As the AI landscape continuously evolves, understanding the foundational and emerging aspects of deep learning becomes crucial for professionals looking to stay ahead in tech-related fields. This course helps to bridge that gap by offering updated insights on how to leverage the power of TensorFlow applications. Discover the latest features and best practices of TensorFlow with practical applications and real-world examples. Along the way, instructor Isil Berkun introduces you to generative AI concepts to inspire further exploration and learning. By the end of this course, you’ll be proficient in implementing deep learning models with TensorFlow.
Skills covered
TensorFlowNeural Networks and Deep LearningGoogleArtificial Intelligence (AI)One-Off
Concepts
0. Introduction
- 01 - Welcome to deep learning with TensorFlow
1. Diving into Codespaces
- 02 - Codespaces - Your ready-to-use workspace
- 03 - Setting up TensorFlow with Codespaces
2. Understanding TensorFlow
- 04 - TensorFlow essentials
- 05 - TensorFlow simplified for NumPy users
- 06 - Machine learning workflow
- 07 - Challenge - Normalizing tensors
- 08 - Solution - Step-by-step statistical analysis
3. Building Your First Model
- 09 - Let's create a TensorFlow model
- 10 - Preprocessing and feeding data into your model
- 11 - Monitor training and validation
- 12 - Success metrics
- 13 - Save and reuse trained models
- 14 - Autoencoders - A gentle introduction to generative models
- 15 - Challenge - My first TensorFlow model
- 16 - Solution - Building blocks to TensorFlow mastery
4. Mastering TensorBoard
- 17 - Visualizing success with TensorBoard
- 18 - Deep dives into training metrics
Conclusion
- 19 - Next steps