AI & Data · Advanced Specialist
Neural Networks
Trace the gradient. Diagnose the curve. Transfer what works.
Build deep-learning intuition by implementing and tuning networks across vision, sequence and transformer tasks.
Advanced layer includedIndustry Edge tools and scenario-based labs beyond the recognized core. Curriculum v1.1 • Updated Jul 2026
Recognized curriculum, prerequisites, assessments, and public fees disclosed before counselling.
Industry Edge Lab includedAdvanced tools, emerging patterns, and scenario-based labs beyond the recognized core.Registration and admissions follow-up continue securely in BharatCampus ONE.
Included beyond the published curriculum
The recognized core is your foundation—not the finish line.
Every Neural Networks cohort also enters the CohortAI Industry Edge Lab: an evolving layer of advanced tool categories, emerging architecture and production patterns, and scenario-based labs. A guided counselling preview connects the most relevant capabilities to your target role, followed by a release-governed lab sequence after enrollment.
Release-governed advanced tools Emerging architecture patterns Scenario-based labs
Fit before enrollment
Who should join
Learners who have completed Machine Learning Concepts or possess equivalent Python, statistics and ML foundations.
Role pathways
Your starting point
Python, NumPy, basic calculus/probability and supervised-learning concepts.
This is not an exclusion filter. It helps us confirm whether you can start directly or would benefit from a short bridge before the cohort.Recognized core
Skills and technology stack
These employability foundations are part of the public learning promise and remain visible before registration.
Python PyTorch/TensorFlow Tensors Backpropagation CNNs Sequence models Attention Transformers Transfer learning Experiment tracking concepts
Build-first curriculum journey
7 missions. One defensible portfolio.
Mentor-led hybrid learning with 1.5-hour live instruction, all-day supervised practice access, weekly build evidence and a version-locked capstone.
M012 weeks · 15 live hours
Tensors, calculus intuition and backpropagation
Gradient Detective: locate a silent shape or derivative error.
- Tensor shapes and broadcasting
- Linear algebra for layers
- Derivatives, gradients and chain rule
- Computation graphs
Guided labImplement a small neural network and verify gradients against automatic differentiation.
Portfolio evidenceFrom-scratch notebook, shape trace and gradient-check report.
AssessmentTensor-shape and backprop practical.
M021 week · 7.5 live hours
Multilayer perceptrons and training loops
Learning Curve Clinic: diagnose the model from curves before touching hyperparameters.
- Dense layers and activations
- Initialisation
- Loss and optimiser choices
- Mini-batches and epochs
Guided labBuild a reusable training loop and compare optimisation choices on a tabular or image task.
Portfolio evidenceTraining framework, learning curves and experiment report.
AssessmentTraining-loop code review.
M032 weeks · 15 live hours
Convolutional neural networks
Model Vision Board: show what the network focuses on and where it fails.
- Convolution, filters and receptive fields
- Padding, stride and pooling
- CNN architectures
- Image augmentation
Guided labTrain and analyse an image classifier with augmentation and interpretable failure cases.
Portfolio evidenceVision model, confusion/error gallery and model card.
AssessmentVision-model diagnosis challenge.
M042 weeks · 15 live hours
Sequence models, attention and transformers
Context Window Lab: discover what the model remembers, ignores and confuses.
- Sequence data and embeddings
- RNN/LSTM/GRU intuition
- Attention mechanism
- Transformer blocks
Guided labBuild a sequence classifier or small attention model and compare recurrent and attention approaches.
Portfolio evidenceSequence notebook, architecture comparison and error analysis.
AssessmentAttention and sequence reasoning viva.
M051 week · 7.5 live hours
Optimisation and regularisation
Ablation Arena: change one factor at a time and prove what actually helped.
- SGD, momentum and adaptive optimisers
- Learning-rate schedules
- Weight decay and dropout
- Batch/layer normalisation
Guided labRun a controlled optimisation study and explain which intervention improved generalisation.
Portfolio evidenceExperiment matrix, checkpoints and tuning decision memo.
AssessmentAblation-study review.
M061 week · 7.5 live hours
Transfer learning
Small Data, Strong Model: achieve value without training from scratch.
- Pre-trained models and feature reuse
- Frozen versus fine-tuned layers
- Domain shift and data size
- Learning rates and discriminative tuning
Guided labAdapt a pre-trained model to a small domain dataset and compare strategies.
Portfolio evidenceTransfer-learning model, adaptation report and deployment note.
AssessmentAdaptation-strategy viva.
M071 week · 7.5 live hours
Neural-network capstone
Neural Demo Day: prove learning quality, failure awareness and deployment feasibility.
- Problem and data strategy
- Architecture and baseline
- Training and experiment tracking
- Evaluation and interpretation
Guided labDeliver a vision, text or sensor deep-learning solution with reproducible experiments and failure analysis.
Portfolio evidenceCapstone repository, trained model, model card, experiment report and demo.
AssessmentDeep Learning Review Board and interview simulation.
The week feels different here
A repeatable rhythm from concept to evidence
Weekly rhythm
- 1Concept sprint and visual roadmap
- 2Guided implementation lab
- 3Independent build challenge
- 4Debug, review and production-pattern clinic
- 5Skill check, demo and learning reflection
Learner experience
CohortAI Skill Passport with milestone badges
Build-first weekly checkpoints rather than lecture-only completion
Peer demo and code/design review rituals
Mentor office hours and all-day practice-lab access
Capstone Demo Day with a business narrative, technical walkthrough and interview-style defence
Responsible AI-assisted learning policy: explain, verify and own every submitted artifact
Interview-ready proof
What you will be able to show—not merely claim
These are tangible outputs you can demonstrate, explain, and defend through a technical walkthrough, business narrative, or interview conversation.
Neural network from core operations
Image classification model
Sequence/attention prototype
Transfer-learning solution
Deep-learning capstone
Transparent evaluation
How your progress is assessed
Completion standard
Minimum 70% overall, mandatory capstone pass, at least 80% lab submissions and versioned portfolio evidence.
CohortAI Industry Edge Lab
Your published curriculum is the foundation. The Industry Edge is what keeps it moving forward.
The Deep Learning Frontier Lab extends the program with efficient adaptation, multimodal representation and deployment-optimisation patterns.
Your counselling preview connects relevant capability categories to your target role. The complete release-governed lab sequence becomes part of the enrolled learning plan.Version-controlled learning promise
NEURAL-NETWORKS@1.1.0
Released 24 July 2026 under catalog CAT-2026.09.1. This page presents the approved curriculum, learner outcomes, duration, and public fee for informed comparison.
Straight answers
Frequently asked questions
Who is the Neural Networks program designed for?
Learners who have completed Machine Learning Concepts or possess equivalent Python, statistics and ML foundations.
What prerequisites do I need?
Python, NumPy, basic calculus/probability and supervised-learning concepts.
What is included in the public curriculum?
The public curriculum includes the recognized stack, all 7 module themes, guided labs, portfolio artifacts, assessment model, duration, and public fees. The Industry Edge Preview explains how advanced capability categories extend this foundation for your target role.
Where do counselling and enrollment happen?
CohortAI supports public discovery and program comparison. Registration, consent, counselling follow-up, application updates, and enrollment are managed securely in BharatCampus ONE.