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Cloudfuturex

Build Neural Networks That Actually Work

Our six-month intensive program starts September 2025. You'll move from theory to production-ready models through hands-on projects. Most students deploy their first working neural network within eight weeks.

View September 2025 Schedule
Neural network programming workspace with code and visualizations
01

Foundation Phase

Weeks 1-8 focus on core concepts. You'll write your first neural network from scratch, no frameworks. Just Python and math.

  • Forward and backward propagation mechanics
  • Gradient descent implementation from zero
  • Building activation functions manually
  • Debug common training failures
  • First working model by week 7
02

Framework Mastery

Weeks 9-16 introduce PyTorch and TensorFlow. Now that you understand what's happening under the hood, frameworks make sense.

  • Convolutional networks for image work
  • Recurrent architectures for sequences
  • Transfer learning with pre-trained models
  • Handle real datasets with noise and gaps
  • Deploy models to cloud environments
03

Production Project

Weeks 17-24 are dedicated to your capstone. Build something that solves an actual problem, not just a tutorial exercise.

  • Work with real client requirements
  • Handle performance optimization challenges
  • Document your architecture decisions
  • Present technical results to non-technical stakeholders
  • Portfolio piece you'll actually be proud of

Learn From People Who've Been There

Both instructors have spent years debugging neural networks in production. They know where students get stuck because they got stuck there too.

Instructor Aldric Sorensen

Aldric Sorensen

Lead Technical Instructor

Aldric built computer vision systems for manufacturing defect detection. He's debugged gradient explosions at 3 AM more times than he'd like to remember. His students appreciate that he explains why models fail, not just how they work when everything goes right.

Instructor Saskia Torvald

Saskia Torvald

Applied ML Specialist

Saskia worked on recommendation systems that handled 2 million users daily. She teaches the unglamorous but essential stuff - data cleaning, handling edge cases, and keeping models maintainable when you're the only person who understands the codebase.