SR Digital Academy-SAP, Data Science & AI Training Institute

Deep Learning Training Institute in Dilsukhnagar Hyderabad

Welcome to SR Digital Academy – SAP, Data Science & AI Training Institute, a leading Deep Learning training institute in Dilsukhnagar, Hyderabad. Our Deep Learning Course is designed for students, graduates, software professionals, data analysts, machine learning engineers, and AI enthusiasts who want to master neural networks and advanced Artificial Intelligence technologies.

Deep Learning is a specialized branch of Artificial Intelligence and Machine Learning that enables computers to learn from large volumes of data and solve complex problems such as image recognition, speech recognition, natural language processing, autonomous systems, and predictive analytics. This comprehensive course covers Neural Networks, TensorFlow, Keras, Computer Vision, NLP, CNN, RNN, LSTM, Transformers, and Generative AI concepts.

At SR Digital Academy, learners gain practical experience through real-time projects, hands-on coding exercises, industry case studies, and placement-focused training to become job-ready Deep Learning professionals.

Why Choose SR Digital Academy for Deep Learning Training?

  • Industry-oriented Deep Learning curriculum

  • Hands-on practical implementation

  • Real-time AI and Deep Learning projects

  • Experienced AI trainers

  • Interview preparation and placement assistance

  • Classroom and online training options

  • Certification support

  • Resume building and career guidance

Deep Learning Course Content

Module 1: Introduction to Deep Learning

  • Introduction to Artificial Intelligence

  • Introduction to Machine Learning

  • What is Deep Learning?

  • Deep Learning Applications

  • AI Industry Trends

  • Career Opportunities in Deep Learning

Module 2: Python Programming for Deep Learning

  • Python Fundamentals

  • Variables and Data Types

  • Functions and Modules

  • NumPy

  • Pandas

  • Matplotlib

  • Data Visualization

Module 3: Mathematics for Deep Learning

  • Linear Algebra

  • Matrices and Vectors

  • Probability and Statistics

  • Calculus Fundamentals

  • Optimization Techniques

  • Gradient Descent

Module 4: Data Preprocessing

  • Data Collection

  • Data Cleaning

  • Missing Value Handling

  • Feature Engineering

  • Data Transformation

  • Data Visualization

  • Data Splitting Techniques

Module 5: Neural Networks Fundamentals

  • Biological Neurons vs Artificial Neurons

  • Perceptron Model

  • Artificial Neural Networks (ANN)

  • Activation Functions

  • Forward Propagation

  • Backpropagation

  • Loss Functions

Module 6: Deep Learning Frameworks

  • Introduction to TensorFlow

  • TensorFlow Architecture

  • Keras Fundamentals

  • Building Neural Networks

  • Model Training

  • Model Evaluation

  • Model Optimization

Module 7: Artificial Neural Networks (ANN)

  • ANN Architecture

  • Hidden Layers

  • Output Layers

  • Hyperparameter Tuning

  • Model Optimization

  • ANN Projects

Module 8: Convolutional Neural Networks (CNN)

  • Introduction to Computer Vision

  • Image Processing Basics

  • CNN Architecture

  • Convolution Layers

  • Pooling Layers

  • Image Classification

  • Object Detection Fundamentals

Module 9: Recurrent Neural Networks (RNN)

  • Sequence Data Processing

  • RNN Architecture

  • Time Series Analysis

  • Sequential Learning

  • Text Processing

  • Prediction Models

Module 10: Long Short-Term Memory (LSTM)

  • LSTM Architecture

  • Sequence Prediction

  • Time Series Forecasting

  • Stock Market Prediction

  • Sentiment Analysis Applications

Module 11: Natural Language Processing (NLP)

  • Introduction to NLP

  • Text Preprocessing

  • Tokenization

  • Word Embeddings

  • Text Classification

  • Sentiment Analysis

  • Language Modeling

Module 12: Transformers and Modern AI Models

  • Introduction to Transformers

  • Attention Mechanism

  • BERT Fundamentals

  • GPT Models Overview

  • Large Language Models (LLMs)

  • Modern AI Applications

Module 13: Generative AI Fundamentals

  • Introduction to Generative AI

  • Large Language Models

  • ChatGPT Applications

  • Prompt Engineering

  • AI Content Generation

  • AI Automation

Module 14: Model Deployment

  • Saving and Loading Models

  • Flask Basics

  • Streamlit Applications

  • API Integration

  • Cloud Deployment Overview

  • Production Best Practices

Module 15: Real-Time Deep Learning Projects

Beginner Projects

  • Handwritten Digit Recognition

  • House Price Prediction

  • Customer Classification

Intermediate Projects

  • Image Classification System

  • Sentiment Analysis Application

  • Face Mask Detection System

Advanced Projects

  • Face Recognition Attendance System

  • AI Chatbot Development

  • Object Detection System

  • Deep Learning Business Analytics Platform

Tools & Technologies Covered

  • Python

  • NumPy

  • Pandas

  • Matplotlib

  • TensorFlow

  • Keras

  • OpenCV

  • Scikit-Learn

  • Jupyter Notebook

  • Google Colab

  • Hugging Face

  • Streamlit

  • Git & GitHub

Course Duration

  • Fast Track Program – 2 Months

  • Regular Program – 3 Months

  • Advanced Program – 4 Months

Career Opportunities After Deep Learning Training

  • Deep Learning Engineer

  • AI Engineer

  • Machine Learning Engineer

  • Data Scientist

  • Computer Vision Engineer

  • NLP Engineer

  • Generative AI Engineer

  • AI Research Associate

  • Artificial Intelligence Consultant

Who Can Enroll?

  • Students and Freshers

  • Data Analysts

  • Python Developers

  • Machine Learning Professionals

  • Software Engineers

  • Data Science Aspirants

  • AI Enthusiasts

Certification

Upon successful completion of the Deep Learning Course, students will receive a Deep Learning Certification from SR Digital Academy along with project experience certification and placement assistance support.

Join the best Deep Learning Course Training in Dilsukhnagar Hyderabad at SR Digital Academy – SAP, Data Science & AI Training Institute and build a successful career in Artificial Intelligence, Machine Learning, and Generative AI.