Welcome to the Deep Learning Specialization repository! This repo captures my journey through the Deep Learning Specialization by Prof. Andrew Ng on Coursera. 🚀
🌍 "872,517 learners, 134,127 reviews, and a rating of 4.9 stars" — This specialization is widely acclaimed and known for its hands-on approach to deep learning!
The Deep Learning Specialization is a foundational course that explores the capabilities, challenges, and opportunities of deep learning. Throughout this journey, I developed various architectures, from basic neural networks to advanced models like CNNs, RNNs, and Transformers. This repo includes resources and solutions to help others navigate this challenging yet rewarding course.
Modules:
- Neural Networks and Deep Learning
- Improving Deep Neural Networks (Hyperparameter Tuning, Regularization, Optimization)
- Structuring Machine Learning Projects
- Convolutional Neural Networks
- Sequence Models
- Master Neural Network Architectures: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), and Transformers.
- Optimization Techniques: Techniques like Dropout, BatchNorm, and Xavier/He initialization for improved performance.
- Real-World Applications: NLP, image recognition, music synthesis, and more.
- Framework: Implementation using Python and TensorFlow, with project-based learning.
🌐 New! Check out this Notion document for my notes and additional insights from the course.
| Section | Description |
|---|---|
| 📚 Course Notes | Key takeaways and detailed notes for each course. |
| 💻 Programming Assignments | Complete code for all programming exercises. |
| ✏️ Quiz Solutions | Quiz answers and explanations to help with tough concepts. |
- Feedforward Neural Networks
- Convolutional Neural Networks (CNNs) for image-related tasks
- Recurrent Neural Networks (RNNs) and variants (LSTM, GRU) for sequential data
- Transformer Models using HuggingFace for advanced NLP
Each model includes data pre-processing, model building, training, and evaluation steps, along with optimizations for bias and variance.
Q: Can I use the models from this repository in a production environment?
A: This repository is intended for learning and experimentation. Always check with the course’s terms of use on Coursera before applying any of these models commercially.
Q: How should I approach the quizzes and assignments?
A: Begin by watching the lecture videos carefully and trying each assignment independently. If you're stuck, refer to the detailed notes and solutions provided here.
Q: How long does it take to complete each course?
A: The time required varies based on your experience and schedule, but on average, each course may take about 1-2 weeks to complete.
Q: Where can I access additional notes and resources?
A: You can find my comprehensive notes and insights on this Notion document, which includes additional references, key points, and useful links from each course module.
| Version | Date | Changes |
|---|---|---|
| 1.0 | 2024-09 | Start learing and compelter first course. |
| 2.0 | 2024-11 | Complete all 5 course: quizz, programming exercise |
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Contact Me
Feel free to reach out with any questions, suggestions, or feedback! I’m always interested in improving this resource to support other learners.
