Embarking on a journey to grasp the essentials of deep learning in just two days? Here is a compact and intensive study plan that is designed to give you a strong start.
- Basic understanding of Python programming
- Foundational knowledge in mathematics, particularly linear algebra and calculus
- Watch introductory videos/lectures
- Understand the differences between supervised and unsupervised learning
- Work on Python coding exercises focusing on libraries like NumPy and pandas
- Learn about the architecture of neural networks
- Understand different activation functions
- Implement a simple neural network using a deep learning framework such as TensorFlow or PyTorch
- Learn the mathematical fundamentals behind backpropagation
- Watch lectures/tutorials explaining the concept in detail
- Work on exercises to implement backpropagation from scratch
- Learn about the applications and architecture of CNNs
- Start with simple image classification projects using CNNs
- Understand the architecture and applications of RNNs
- Implement simple projects, like text generation using RNNs
- Familiarize yourself with deep learning frameworks like TensorFlow and PyTorch
- Continue working on your hands-on projects
- Try to implement what you learned during the day
- Project Completion: Finish any remaining project work
- Resource Gathering: Compile a list of resources (books, courses, etc.) for deeper learning
- Community Engagement: Begin engaging with the community by joining relevant forums and groups
- Breaks: Ensure to take short breaks between sessions to avoid burnout
- Practical Implementation: Focus on hands-on experience; the more you practice, the better you understand
- Documentation: Document your learning process; it will be a helpful resource in the future