Deep Learning Course in Hyderabad
Build and train neural networks for images, text and sequences with PyTorch and TensorFlow
The Deep Learning course teaches you to design, train and improve neural networks. You begin with Python, NumPy and the mathematics of learning, then build neural networks from scratch to understand forward and backward propagation, loss functions, optimisers, initialisation, normalisation and regularisation. You then use PyTorch, with TensorFlow and Keras for comparison, to build convolutional neural networks for images, recurrent networks and LSTMs for sequences, and transformer-based models for text, and apply transfer learning with pre-trained models. Further modules cover training practice such as learning-rate schedules, mixed precision, data augmentation, experiment tracking and debugging, autoencoders and generative models at an introductory level, model export with ONNX, and serving with FastAPI. The course suits graduates and professionals with basic Python and machine learning knowledge; the Machine Learning course is good preparation. Labs run on free GPU notebooks such as Google Colab. You build several models and a capstone. Classes run in Ameerpet, Hyderabad, and in live online batches.
Syllabus
Deep Learning Foundations & Setup
- •From machine learning to deep learning: when neural networks help
- •Tensors, automatic differentiation and GPU basics in PyTorch
- •Colab, environments and reproducibility
- •Lab: Build tensor operations and a first autograd example
- •Outcome: Set up a working deep learning environment
Maths for Neural Networks
- •Vectors, matrices and tensor shapes
- •Derivatives, gradients and the chain rule
- •Probability, likelihood and cross-entropy intuition
- •Lab: Compute gradients by hand and verify with autograd
- •Outcome: Understand why training works
Neural Networks from Scratch
- •Perceptron, multilayer networks and activation functions
- •Backpropagation and gradient descent
- •Loss functions for regression and classification
- •Lab: Implement a small neural network in NumPy
- •Outcome: Explain every step of training a network
Training with PyTorch
- •nn.Module, DataLoader and training and validation loops
- •Optimisers: SGD, momentum, Adam and AdamW; learning-rate schedules
- •Checkpointing, early stopping and logging
- •Lab: Train a classifier on a standard dataset
- •Outcome: Write clean, reusable training code
Regularisation & Optimisation Practice
- •Overfitting, underfitting and diagnosing learning curves
- •Dropout, batch and layer normalisation, weight decay and data augmentation
- •Initialisation, vanishing and exploding gradients, mixed precision
- •Lab: Improve a failing model step by step
- •Outcome: Systematically fix training problems
Convolutional Neural Networks
- •Convolution, padding, stride and pooling
- •Architectures: LeNet, VGG, ResNet and EfficientNet ideas
- •Transfer learning and fine-tuning with torchvision
- •Lab: Fine-tune a pre-trained CNN on a custom image dataset
- •Outcome: Build accurate image classifiers with limited data
Sequence Models: RNN, LSTM & GRU
- •Recurrent networks and the vanishing gradient problem
- •LSTM and GRU architectures
- •Sequence tasks: time series and text classification
- •Lab: Forecast a time series with an LSTM
- •Outcome: Model sequences with recurrent networks
Attention & Transformers
- •Attention mechanism and self-attention
- •Transformer architecture and positional encoding
- •Using pre-trained transformers with Hugging Face; fine-tuning for classification
- •Lab: Fine-tune a transformer for text classification
- •Outcome: Understand and apply transformer models
TensorFlow & Keras
- •Keras Sequential and Functional APIs
- •Callbacks, TensorBoard and saving models
- •Comparing PyTorch and TensorFlow workflows
- •Lab: Rebuild a model in Keras and compare
- •Outcome: Read and write code in both major frameworks
Autoencoders & Generative Models Intro
- •Autoencoders for compression and anomaly detection
- •Variational autoencoders and GAN concepts
- •Diffusion models overview
- •Lab: Build an autoencoder for anomaly detection
- •Outcome: Understand the main generative approaches
Experiment Tracking, Export & Serving
- •Tracking experiments with TensorBoard, MLflow or Weights & Biases
- •Exporting with TorchScript or ONNX; inference optimisation basics
- •Serving with FastAPI and Docker
- •Lab: Serve a trained model as an API
- •Outcome: Move a model from a notebook to a service
Capstone Project & Interview Preparation
- •Capstone: choose an image, text or sequence problem and take it to a deployed model
- •Reproducible repository, model card and presentation
- •Interview practice on fundamentals and system choices
- •Outcome: A portfolio-ready deep learning project
Tools Covered
Projects Included
- Neural Network from Scratch: Implement forward and backward passes in NumPy and compare with PyTorch; deliverable is a documented notebook.
- Image Classifier with Transfer Learning: Fine-tune a CNN on a custom dataset with augmentation; deliverable is a trained model and error analysis.
- Time Series Forecaster: Build an LSTM forecaster and compare it with a baseline; deliverable is a notebook with metrics.
- Text Classifier with Transformers: Fine-tune a pre-trained transformer for sentiment or intent; deliverable is a model and evaluation report.
- Capstone Deployed Model: Take a chosen deep learning model to a FastAPI service with tracking and a model card; deliverable is a GitHub repository and demo.
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Call: +91 9908099980Prerequisites & Eligibility
Understand the foundation and skills needed to succeed in the Deep Learning course.
Course Prerequisites
- Any graduate is eligible for enrollment across all program tracks
- Dedication to work on real-time scenario projects and live client project implementations
- Willingness to engage in aptitude, soft skills and interview readiness sessions
- Commitment to complete full-day training or intensive/internship program schedules
Why Choose GloryTecks
Build practical capabilities through structured learning, expert mentors and hands-on projects.
5+ Years of Excellence
Established training institute in Hyderabad with a proven track record of producing industry-ready professionals.
Expert Faculty
Learn from trainers with real-world industry experience, who teach the way the work is actually done.
Placement Assistance
A dedicated placement team with tie-ups across hiring companies. We support you until you get hired.
Comprehensive Curriculum
Updated syllabus covering the latest technologies and industry best practices, with hands-on projects.
Career Growth Focus
Not just training, but complete career transformation with soft skills and interview preparation.
Online & Classroom
Attend live online batches or classroom training at our Ameerpet centre, whichever suits you.
Flexible Batches
Weekday, weekend and online batch timings to suit working professionals, students and freshers.
Real-time Projects
Work on real-time scenario projects so you finish with practical experience to show employers.
We provide placement support with direct access to hiring companies, along with comprehensive training that helps freshers become job-ready professionals.
Certification
Explore certification and practical learning opportunities with a focus on Deep Learning.
Certificate of Completion
Deep Learning
This certifies that
[Your Name]
has successfully completed the
Deep Learning
Training Program
GloryTecks
Ameerpet, Hyderabad
Certificate ID
GT-YYYY-XXXX
Certificate Includes
- Course completion certificate from GloryTecks
- Project completion certificates
- Internship completion certificate for eligible internship programs
- Digital certificate with a unique verification ID
- Shareable on LinkedIn and other professional platforms
- Valid proof of skill acquisition for employers
- Includes the detailed syllabus covered
Industry Focus
A certificate built around the tools and skills employers ask for, adding credibility to your resume.
Skill Validation
Proves your expertise in the course and the technologies you worked with.
Career Advancement
Strengthens your profile when you apply for jobs and negotiate your salary.
Digital & Physical
Get a digital certificate for online sharing and a physical copy for framing.
Course Certificate
Upon successful completion of the training program
Project Certificate
For major projects completed during training
Course Features & Highlights
Build a strong foundation through a structured Deep Learning course with practical learning and hands-on exposure.
Live Projects
Work on real-world projects from day one with industry use cases.
Expert Trainers
Learn from industry professionals with real-world experience.
Placement Support
Dedicated placement team with assistance until you get hired.
Industry Certification
Get certified and boost your resume with recognized credentials.
LMS Access
Access to learning materials and recorded sessions.
Mock Interviews
Weekly mock interviews to prepare you for real job scenarios.
Resume Building
Professional resume preparation and LinkedIn profile optimization.
Additional Benefits
- Flexible Payment Plans
- 6-12 Months LMS Access
- Doubt Clearing Sessions
- 24/7 Learning Support
Frequently asked questions
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PyTorch or TensorFlow: which is used?
How is Deep Learning different from the Computer Vision and NLP courses?
Is the maths difficult?
Will I train large language models?
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Best Deep Learning Training in
Ameerpet, Hyderabad
Located at Ameerpet — Hyderabad’s IT training hub. Easily accessible from all areas of Hyderabad. Just 2 minutes walk from Ameerpet Metro Station.
Placement Support
Career support covers resume and LinkedIn preparation, mock interviews and a portfolio of 5 course projects. Roles that use these skills include Deep Learning Engineer (entry level), Machine Learning Engineer, AI Engineer, Computer Vision or NLP Engineer (entry level); support is guidance with the job search, not a guarantee of a job.