Neural Network Basics
Perceptrons, activation functions, forward pass, backpropagation, gradient descent, loss functions, and optimization.
Deep Learning Course
Master Neural Networks for real AI careers.
Learn Deep Learning from fundamentals to production-ready projects. Build CNN, RNN, Transformer, computer vision, NLP, and generative AI foundations with hands-on mentorship from AI Fusion-X.
Classroom + Online
Real Projects
Placement Support
TensorFlow + PyTorch + Projects
8+Deep Learning modules
6Portfolio projects
CNNVision model training
NLPTransformer foundations
Deep Learning training in Hyderabad for AI, computer vision, NLP, and GenAI careers
This Deep Learning course in Hyderabad is designed for learners who want to move beyond basic machine learning and build real neural network applications. You will learn how models actually train, why loss functions and optimizers matter, how CNNs process images, how sequence models work, and how Transformer-based systems power modern AI products.
If you are new to the AI path, start with our Python for AI course or Machine Learning course. If your goal is applied analytics and business decision-making, explore our Data Science course. For LLMs, prompt engineering, and modern AI applications, continue into Generative AI training after this course.
What you will learn
Designed for learners who already know Python basics and want to move into AI, computer vision, NLP, and advanced machine learning roles.
CNNs & Computer Vision
Image classification, convolution layers, pooling, transfer learning, data augmentation, and model evaluation.
RNNs, LSTMs & Transformers
Sequence models, attention, embeddings, transformer architecture, text classification, and GenAI foundations.
Tools and frameworks covered
Learn the tools used in real deep learning workflows, from experimentation to deployment-ready demos.
Python
NumPy
Pandas
TensorFlow
Keras
PyTorch
OpenCV
Scikit-Learn
Hugging Face
Matplotlib
Streamlit
GitHub
Deep Learning curriculum
A practical learning path from neural network theory to deployable AI projects.
01
Python, NumPy & ML Refresh
Python workflows, matrix operations, ML recap, train/test split, metrics, and model lifecycle.
02
Neural Network Fundamentals
Neurons, layers, activation functions, loss functions, backpropagation, and gradient descent.
03
TensorFlow & PyTorch
Tensors, datasets, custom models, training loops, callbacks, checkpoints, and GPU-ready workflows.
04
Computer Vision with CNNs
Convolutions, pooling, image classification, transfer learning, augmentation, and error analysis.
05
Sequence Models
RNNs, LSTMs, GRUs, time-series modeling, text preprocessing, embeddings, and sequence classification.
06
Transformers & Attention
Attention mechanism, encoder-decoder ideas, transformer blocks, embeddings, and LLM foundations.
07
Model Tuning & Regularization
Dropout, batch normalization, learning rate schedules, overfitting control, and hyperparameter tuning.
08
Deployment & Portfolio
Model export, inference APIs, Streamlit demos, project documentation, GitHub portfolio, and interview prep.
Why Choose AI Fusion-X for Deep Learning Training?
Industry-focused Deep Learning training designed to help students become job-ready AI professionals.
Industry Experts
Learn from AI Professionals
Our trainers have practical experience building Deep Learning, Computer Vision, NLP and Generative AI applications used in real business environments.
Real Projects
Portfolio Development
Create GitHub-ready projects including CNN models, Image Classification, NLP applications, Medical Image Detection, Transformers and AI deployment projects.
Placement
Career Support
Resume building, LinkedIn optimization, mock interviews, coding practice, aptitude guidance and placement assistance.
Learning path from beginner to portfolio-ready
The course is structured so every concept leads to a practical assignment, then a portfolio project.
Build fundamentals
Refresh Python, arrays, ML metrics, train/test splits, and model thinking.
Train networks
Understand layers, activations, optimizers, backpropagation, and regularization.
Create projects
Work on CNN, NLP, forecasting, and Transformer mini projects with guided reviews.
Show outcomes
Deploy a demo, clean your GitHub, prepare explanations, and practice interview questions.
Where is Deep Learning Used?
Deep Learning powers today's most advanced AI applications across industries.
Healthcare
Medical image diagnosis, disease prediction, drug discovery, radiology automation and healthcare AI systems.
Finance
Fraud detection, risk prediction, customer behavior analysis and intelligent banking systems.
Retail
Recommendation engines, customer segmentation, product search and demand forecasting.
Manufacturing
Quality inspection using computer vision, predictive maintenance and industrial automation.
Automotive
Self-driving cars, object detection, lane detection and intelligent driver assistance systems.
Cyber Security
Threat detection, anomaly detection, malware classification and network security.
Portfolio projects included
Build proof of skill that you can show during interviews and portfolio reviews.
Image Classifier
Train a CNN model to classify image categories and evaluate real-world prediction quality.
Medical Image Detection
Use transfer learning to detect visual patterns and handle class imbalance responsibly.
Text Sentiment Model
Build an NLP model using embeddings and sequence architectures for sentiment analysis.
Time-Series Forecasting
Apply LSTM/GRU models to forecast patterns from sequential business data.
Transformer Mini Project
Understand attention and build a small transformer-based workflow for text tasks.
AI Demo App
Deploy a trained model with a simple UI so recruiters can inspect your work quickly.
Career outcomes after Deep Learning
Deep Learning skills support multiple AI career tracks when combined with strong projects and interview preparation.
Deep Learning EngineerBuild, tune, and deploy neural network models for real AI applications.
Computer Vision EngineerWork on image classification, object detection, visual inspection, and model evaluation.
NLP EngineerUse embeddings, sequence models, and Transformers for text classification and language tasks.
Machine Learning EngineerCombine ML fundamentals with neural networks, deployment, APIs, and production thinking.
Deep Learning Career & Salary
Professionals with Deep Learning skills are among the highest-paid AI specialists.
Freshers
₹5 LPA - ₹8 LPA
2-5 Years
₹10 LPA - ₹20 LPA
Senior AI Engineers
₹25 LPA+
Who Should Join This Course?
Engineering Students
Build AI career before graduation.
Software Developers
Move into Artificial Intelligence roles.
Data Scientists
Learn Neural Networks and Transformers.
Working Professionals
Upskill for better salary and AI jobs.
Start from ML basics
Get a bridge from Python and ML into neural networks without getting lost in heavy math.
Upgrade to AI roles
Build hands-on confidence for AI Engineer, ML Engineer, CV/NLP Engineer, and Data Scientist roles.
Job-ready outcomes
Every major topic connects to assignments, GitHub work, demos, and interview preparation.
Ready to learn Deep Learning?
Talk to AI Fusion-X counsellors and get batch timings, fees, classroom/online options, and project details.
Frequently asked questions
Quick answers before you join the Deep Learning course.
Do I need Machine Learning before Deep Learning?
Python basics and ML fundamentals are helpful. The course includes a quick ML refresh before moving into neural networks.
Will I learn TensorFlow or PyTorch?
Yes. You will work with both TensorFlow/Keras and PyTorch style workflows, with focus on practical model building.
Are projects included?
Yes. You will build computer vision, NLP, sequence modeling, and model deployment projects for your portfolio.
Is placement support included?
Yes. AI Fusion-X provides resume guidance, interview prep, project presentation support, and career guidance.
Which course should I take after Deep Learning?
If you want LLM and AI app development, continue with the Generative AI course. If you want deployment and cloud workflows, explore Cloud + ML.
Is this course useful for computer vision?
Yes. CNNs, transfer learning, OpenCV workflows, image classification, and model evaluation are included in the Deep Learning curriculum.