Data & Analytics
Neural networks, built and trained from the ground up.
Deep Learning covers neural network architectures properly: what each layer does, why gradients vanish, how a convolution actually sees an image, and what a transformer attends to.
Overview
Deep Learning covers neural network architectures properly: what each layer does, why gradients vanish, how a convolution actually sees an image, and what a transformer attends to.
You implement networks in TensorFlow, Keras and PyTorch, train on real image and sequence datasets, use transfer learning to get production results without a datacentre, and deploy a trained model as a working inference service.
Curriculum
The same structure on every Xypron course page, so you can compare tracks like for like. Each module ends in working code, not a quiz.
What a network is doing between input and loss.
Everything that goes wrong at depth, and the fixes.
Vision models, from first convolution to modern architectures.
Data where order carries the meaning.
The three toolkits you will be asked about by name.
Getting a trained network in front of a user.
Tools you'll master
Python TensorFlow Keras PyTorch 
NumPy OpenCV 
Jupyter 
Git Outcomes
The course does not end at the capstone. Placement support runs from your first mock interview until you have an offer — and it is support, not a guarantee.
Rebuilt around the projects you actually shipped here, targeted at the roles you are applying for.
Technical and HR rounds with written feedback after each one, repeated until the gaps close.
Your profile goes to our hiring partners with a note from the trainer who taught you.
We book the interviews and follow through until there is a decision either way.
Next step
Batch timings, the fee, whether your background fits, what the trainer's day job is — all fair questions. A counsellor will call you back within two business hours.
Python fluency and prior exposure to machine learning fundamentals. The Machine Learning track is the recommended precursor.
The course runs for 3 months in classroom & online mode. Weekday, weekend and evening batches are scheduled continuously — call +91 73493 01293 for the next available start date.
Image classifier with transfer learning on ResNet; Sequence forecasting model with LSTM; Deployed inference API for a trained vision model — plus an end-to-end capstone project you deploy yourself and can walk an interviewer through line by line.
Yes. Sit a full session with the trainer who would actually teach your batch before you decide anything. Ask for one on WhatsApp or through the enquiry form on this page.
Enquiry
Choose WhatsApp or email below. We'll open it with your details already filled in for Deep Learning — you just hit send.
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Also in Data & Analytics
The Data Analyst full stack course covers the complete analysis workflow: collect, clean, explore, visualise, and then communicate the finding to someone who has to act on it. That last step is what most analyst candidates cannot do, and it is what gets you hired.
The Data Science full stack course runs the complete workflow: collect, clean, explore, model, evaluate, communicate. It is deliberately broader than the Data Analyst track — you do not stop at the dashboard, you build the predictive model behind it.
This track goes deep on the machine learning layer alone — algorithm by algorithm, with the mathematics explained and then implemented rather than imported and hoped for.
Next step
Sit a free demo class before you commit. Ask about batch timings, the trainer, and exactly what the placement support covers.
