Embark on a comprehensive journey into Python deep learning with this beginner-friendly tutorial video. Set up your machine, explore essential tools and libraries, and progress from novice to successfully running an image classification model. Learn to install Anaconda, work with Jupyter notebooks, create and manage development environments, utilize Git and GitHub, install TensorFlow, configure GPU for deep learning, and train a deep image classifier. Follow along with provided code and resources, including links to necessary software and documentation. Gain practical experience through hands-on exercises and emerge with a solid foundation in deep learning fundamentals.
Getting Started with Python Deep Learning for Beginners
Overview
Syllabus
- Start
- PART 1: Setting up Python and Jupyter with Anaconda
- Installing Anaconda
- Working with Jupyter
- PART 2: Environment Creation Workflows
- Working with Git and GitHub
- Creating Environments for DL
- Activating a Virtual Environment
- PART 3: Installing Tensorflow for Deep Learning
- Running the Image Classifier Pipeline
- PART 4: Configuring your GPU
- PART 5: Training a Deep Image Classifier
Taught by
Nicholas Renotte
