Beginner
Foundations of Neural Architecture
Covers feed-forward networks, activation functions, and backpropagation from scratch. You will build and train small models before moving to deeper structures.
View ProgramNeural Networks & Architecture Courses
Six structured tracks covering everything from foundational perceptrons to production-scale transformer design — available as group cohorts or one-on-one sessions.
6 Available Tracks
214 Reviews
4.7 Avg Rating
Live Instructor Sessions
What's Currently Available
Each course below runs as a group cohort or can be taken privately. Curriculum is built around real architecture decisions, not abstract theory. Practical work with tools like QBO intuit-style dashboards for tracking progress is part of how we keep things measurable.
Beginner
Intermediate
Convolutional Networks in Practice
Walks through CNN design choices — kernel sizing, pooling strategies, and feature map visualization. Includes transfer learning with pre-trained weights on real datasets.
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Intermediate
Recurrent Networks and Sequence Modeling
Covers LSTM and GRU architectures with a focus on time-series and text data. Students work through vanishing gradient problems hands-on rather than just reading about them.
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Advanced
Transformer Architecture Deep Track
Attention mechanisms, positional encodings, and multi-head self-attention explained through implementation. Builds toward understanding large model design tradeoffs at scale.
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Advanced
Generative Models and GAN Design
GANs, VAEs, and diffusion basics — how they differ structurally and where each fits in practice. Training instability is addressed directly with concrete stabilization techniques.
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Practical
From Model to Production
Covers exporting, serving, and monitoring trained models in real environments. Discusses latency, quantization, and infrastructure choices that rarely appear in academic courses.
See ServicesCommon Questions About Enrollment
A few things people usually ask before signing up.
Basic Python familiarity is helpful but not required for introductory tracks. Foundational modules cover the necessary groundwork before moving into model design. Most students are comfortable by week two.
Group sessions follow a fixed weekly schedule with cohort exercises and peer review. Individual sessions are paced around your availability and shaped around your specific project or career goal — nothing is templated.
Most structured tracks run 8 to 12 weeks with two to three live sessions per week. Self-paced access is included so you can revisit material between sessions without falling behind.
Yes — students occasionally shift formats depending on workload changes. Reach out to info@heraventos.com and we will work out the logistics without you losing progress on the curriculum.
"The transformer track was the first course I took where the instructor actually explained why certain architectural choices exist rather than just showing you the code. It took real effort, but the reasoning finally clicked for me."
Lev Ostrowski ML Engineer — completed Transformer Architecture Track