Build neural networks that see, read and generate
Our Deep Learning course in Hyderabad teaches you to build and train neural networks with PyTorch, so you can move from CNNs and transformers to fine-tuning large models with Hugging Face and deploying them.
Ask SMALL i about this course →How our Deep Learning course in Hyderabad works
We start with tensors and neural networks from scratch, because understanding the basics makes every framework easier. Then you build vision models with CNNs, so you learn how machines see images. Next, you study transformers and Hugging Face, because modern language models are built on them. Later, you fine-tune a language model with LoRA, so it adapts to your own domain. Finally, you export and serve your model, so it works outside the notebook. Because the course is practical, graduates, analysts and developers can all join. Also, our trainers explain every concept in simple language, so beginners do not feel lost. Therefore, you can start even if you have not trained a neural network before.
What is deep learning?
Deep learning uses many-layered neural networks that learn useful features directly from raw images, text and audio. Once you understand four core ideas, most models become easier to follow.
Layers of neurons
Simple units stacked in layers learn increasingly abstract patterns, so deeper networks capture richer features.
Learning by backpropagation
The network measures its error, then adjusts millions of weights to reduce it.
Specialised architectures
CNNs suit images, RNNs suit sequences, and transformers handle language, so each task has a fitting architecture.
Pretrained models
Start from a model trained on huge data, then adapt it to your task.
Because a phone recognises a face, a bank reads a cheque and a chatbot answers in Telugu, all three rely on deep learning. First, you learn how these models are built and trained. Then you fine-tune and serve them, so you understand what sits underneath modern AI.
The six steps of every deep learning project
You will practise this workflow from your first model to a fine-tuned LLM, so each step becomes familiar.
Define
First, state the task, the data and how success will be measured.
Prepare
Next, build datasets, augmentations and data loaders.
Design
Then pick an architecture or a pretrained model to start from.
Train
Next, run training loops with the right loss, optimiser and GPU settings.
Evaluate
Then check validation results, spot overfitting and iterate.
Deploy
Finally, export, optimise and serve the model through an API.
11 modules in our Deep Learning course in Hyderabad
Open any module to see what you will learn, because modules marked Hands on include practical lab work.
01
Introduction to Deep Learning
- AI, Machine Learning and Deep Learning: Where Each Fits
- When to Use Deep Learning and When Classical ML Is Enough
- GPUs, Cloud Notebooks and the Deep Learning Workflow
02
Python, Math and Tensor Foundations
Hands on
- NumPy, Tensors, Broadcasting, Matplotlib
- Linear Algebra, Calculus and Probability for Deep Learning
- Automatic Differentiation and Gradients
03
Neural Networks from Scratch
Hands on
- Perceptron, Activation Functions, Loss Functions
- Forward Pass, Backpropagation, Gradient Descent
- Optimizers: SGD, Adam, AdamW, Learning Rate Schedules
- Dropout, Batch Normalization, Weight Initialization, Regularization
04
PyTorch for Deep Learning
Hands on
- Tensors, nn.Module, Datasets and DataLoaders
- Training Loops, GPU Training, Mixed Precision, torch.compile
- PyTorch Lightning; Keras and TensorFlow Overview
05
Computer Vision with CNNs
Hands on
- Convolution, Pooling, ResNet, EfficientNet
- Transfer Learning, Data Augmentation, Image Classification
- Object Detection with YOLO, Segmentation Overview, Vision Transformers
06
Sequence Models
- RNN, LSTM and GRU
- Text and Time Series with Sequence Models
- Sequence-to-Sequence Models and the Idea of Attention
07
Transformers and Attention
Hands on
- Self-Attention, Multi-Head Attention, Positional Encoding
- Encoder and Decoder Models: BERT and GPT-Style Architectures
- Tokenization, Hugging Face Transformers and Datasets
08
Fine-Tuning Large Language Models
Hands on
- Transfer Learning for Text and Vision
- Parameter-Efficient Fine-Tuning: LoRA and QLoRA with PEFT
- Instruction Tuning, Preference Tuning with TRL, Evaluation
- RAG Overview and When to Fine-Tune Instead
09
Generative Models
- Autoencoders and Variational Autoencoders
- GANs and Diffusion Models with Diffusers
- Multimodal Models Overview
10
Training at Scale and Deployment
Hands on
- Experiment Tracking with Weights & Biases and MLflow
- Distributed Training: DDP, FSDP and DeepSpeed Overview
- Quantization, ONNX Export, Serving with FastAPI, vLLM and Docker
11
Capstone Projects
- Image Classifier, Object Detector, Transformer Text Classifier and Fine-Tuned LLM Projects
- GitHub Portfolio, Resume Building and Interview Preparation
Tools you will use in our Deep Learning course in Hyderabad
These frameworks are used in real projects today, so you also learn what employers expect.
Why learn deep learning
Learn the foundations first, so your skills stay useful as tools and trends change.
Core of modern AI
Image, speech and language models are all built on deep learning, so the skills apply widely.
PyTorch skills
Most research and model code is written in PyTorch, so learning it keeps you close to new work.
Hugging Face ecosystem
Thousands of pretrained models are available, so you can use and fine-tune them with a few lines of code.
Many problem types
Vision, language, audio and generative tasks share the same foundations, so one skill set covers many problems.
Build and ship
Learn quantization, ONNX and serving, because models must run outside the notebook to be useful.
Path to GenAI and agents
A strong base makes LLMs, RAG and agentic AI far easier, so this course is a good step toward them.
Roles you can aim for
Titles vary by company, so treat these as common starting points and next steps.
Deep Learning Engineer
Designs and trains neural networks, then optimises them for speed and accuracy.
PyTorch · PythonComputer Vision Engineer
Builds systems that read images and video, so machines can see and decide.
CNNs · OpenCV · YOLONLP Engineer
Works with text, search and language models, so applications can understand language.
Transformers · Hugging FaceLLM Fine-Tuning Engineer
Adapts large language models to a business domain, so answers fit company needs.
PEFT · LoRA · TRLAI Research Associate
Reproduces ideas from research papers, then tests whether they work in practice.
PyTorch · MathsAI Solutions Developer
Wraps trained models in APIs, so other applications can use them.
FastAPI · DockerWhat you will be able to do
Employers look for technical ability, so the course also builds good professional habits.
Technical skills
- PyTorch: build and train models, then write custom training loops.
- Computer vision: CNNs, transfer learning and object detection, because images need special architectures.
- Transformers: attention and Hugging Face, so you can fine-tune with LoRA.
- Deployment: ONNX, quantization, FastAPI and Docker, so models run in production.
Thinking and communication
- Debugging models: find why a network is not learning, then fix it.
- Experiment discipline: track runs, so you can compare results fairly.
- Resource sense: balance accuracy, speed, memory and cost, because GPUs are expensive.
- Explaining results: describe model behaviour simply, since many listeners are not technical.
Build a portfolio you can show
Because employers want proof, capstone work and interview preparation are part of the course.
Image classifier
Train a CNN first, then improve it with transfer learning.
Object detection
Detect and locate objects in images with a YOLO-style model, so you see vision in practice.
Text classification
Fine-tune a transformer for sentiment or topic labels, then test it on new text.
Fine-tuned LLM
Adapt a language model to a domain with LoRA, so it answers in your chosen style.
Model serving
Export a model, then serve it through an API in Docker.
Portfolio and interviews
Show your models on GitHub, then practise deep learning interview questions.
Who this Deep Learning course in Hyderabad is for
You do not need to be an expert, because the course starts from the basics.
Developers
Programmers moving into AI model building, since they already know how to code.
Data scientists
People who know machine learning and want to go deeper, so this course extends their skills.
Graduates
Students with Python basics, because the early modules cover the foundations.
Curious builders
Anyone ready to practise maths and code regularly, because steady practice matters most.
Deep Learning course in Hyderabad: your questions
If your question is not here, then ask us on WhatsApp or talk to SMALL i.
Do I need to know machine learning first?
It helps but is not required, because the early modules cover the foundations. If you are brand new to data, then our Machine Learning course is a good first step.
Which framework will I use?
PyTorch is the main framework, and you also get an overview of Keras and TensorFlow, so you can read existing code.
Do I need my own GPU?
No, because hands-on work can run on cloud notebooks with GPUs, so a normal laptop is enough.
How is this different from the Generative AI course?
Deep Learning teaches how models are built, trained and fine-tuned, while Generative AI focuses on using LLMs, prompts and building AI applications.
Will I get a job after the course?
No institute can promise a job, so working models in your portfolio are the strongest proof of skill, and we help you build them.
Ready to start learning Deep Learning?
First, ask SMALL i your questions, then meet our trainer in a free live demo.