Uncodemy Deep Learning Course Everything You Need to Know

Month 1 — Daily Schedule:
7:00 AM — Wake up, exercise (30 min)
8:00 AM — Review goals for the day
9:00 AM — Deep work (3 hours)
   - Week 1: Python for AI
   - Week 2: Math foundations
   - Week 3: Machine learning basics
   - Week 4: TensorFlow and PyTorch setup
12:00 PM — Lunch break
1:00 PM — Practice (coding exercises)
2:00 PM — Deep work (2 hours)
5:00 PM — Review progress, plan tomorrow
6:00 PM — Exercise, rest
8:00 PM — Read ML/DL articles (30 min)
10:00 PM — Wind down

Week 1 Review:
□ Can I write Python functions and classes?
□ Do I understand NumPy arrays and Pandas?
□ Have I solved 10+ exercises?

Week 2 Review:
□ Do I understand vectors and matrices?
□ Can I compute gradients conceptually?
□ Do I understand probability basics?

Week 3 Review:
□ Can I build a regression model?
□ Can I build a classification model?
□ Do I understand train-test split?

Week 4 Review:
□ Is TensorFlow installed and working?
□ Is PyTorch installed and working?
□ Have I run a small neural network example?

Month 2 — Daily Schedule:
7:00 AM — Wake up, exercise (30 min)
8:00 AM — Review goals for the day
9:00 AM — Deep work (3 hours)
   - Week 5: Neural network fundamentals
   - Week 6: Training and tuning
   - Week 7: CNN fundamentals
   - Week 8: RNNs and LSTMs
12:00 PM — Lunch break
1:00 PM — Practice (build and train models)
2:00 PM — Deep work (2 hours)
5:00 PM — Review progress, plan tomorrow
6:00 PM — Exercise, rest
8:00 PM — Read DL papers / tutorials (30 min)
10:00 PM — Wind down

Week 5 Review:
□ Do I understand neurons and layers?
□ Can I explain backpropagation?
□ Have I built a simple ANN?

Week 6 Review:
□ Do I understand loss functions?
□ Can I choose an optimizer?
□ Have I used regularization?

Week 7 Review:
□ Do I understand convolutions and pooling?
□ Can I build a CNN?
□ Have I trained an image classifier?

Week 8 Review:
□ Do I understand RNNs and LSTMs?
□ Can I handle sequence data?
□ Have I built a text classifier?

Month 3 — Daily Schedule:
7:00 AM — Wake up, exercise (30 min)
8:00 AM — Review goals for the day
9:00 AM — Deep work (3 hours)
   - Week 9: Transformers and LLMs
   - Week 10: Generative AI
   - Week 11: Deployment
   - Week 12: Capstone project
12:00 PM — Lunch break
1:00 PM — Practice (build, test, deploy)
2:00 PM — Deep work (2 hours)
5:00 PM — Review progress, plan tomorrow
6:00 PM — Exercise, rest
8:00 PM — Read papers / build portfolio (30 min)
10:00 PM — Wind down

Week 9 Review:
□ Do I understand attention?
□ Can I fine-tune a pretrained model?
□ Have I used Hugging Face?

Week 10 Review:
□ Do I understand GANs and diffusion?
□ Have I built an autoencoder?
□ Can I generate simple outputs?

Week 11 Review:
□ Can I wrap a model in an API?
□ Have I deployed on Streamlit?
□ Is my model accessible online?

Week 12 Review:
□ Is my capstone project complete?
□ Is it documented with problem, approach, results?
□ Is it on GitHub?

Daily Tracker:
Date: _________
☐ Morning routine completed
☐ Deep work session 1 (3 hours)
☐ Deep work session 2 (2 hours)
☐ Coding practice completed
☐ Model training / experiment done
☐ Notes taken
☐ Reviewed progress
☐ Planned tomorrow

Today's Accomplishments:
1. 
2. 
3. 

Tomorrow's Priorities:
1. 
2. 
3. 

Weekly Tracker:
Week: _____ (Month 1 / 2 / 3)

Goals for this week:
1. 
2. 
3. 

Daily Progress:
Mon: ☐☐☐
Tue: ☐☐☐
Wed: ☐☐☐
Thu: ☐☐☐
Fri: ☐☐☐
Sat: ☐☐☐
Sun: ☐☐☐

Weekly Wins:
1. 
2. 
3. 

Challenges & Solutions:
1. 
2. 

Next Week's Goals:
1. 
2. 
3. 

Monthly Tracker:
Month: _____ (1 / 2 / 3)

Monthly Goal:
_______________________________________________

Week 1 Progress: ☐☐☐
Week 2 Progress: ☐☐☐
Week 3 Progress: ☐☐☐
Week 4 Progress: ☐☐☐

Monthly Wins:
1. 
2. 
3. 

Topics Mastered:
1. 
2. 
3. 

Models Built:
1. 
2. 
3. 

Projects Completed: _____
Certifications: _____

Next Month's Goal:
_______________________________________________