Description

Course Description:

This course Deep Learning provides a hands-on and theoretical introduction to Deep Learning. Students will learn the mathematical foundations, architectures, and real-world applications of deep neural networks. The course also includes projects using frameworks.

Key Features of Course Divine:

• Collaboration with E‑Cell IIT Tirupati
• 1:1 Online Mentorship Platform
• Credit-Based Certification
• Live Classes Led by Industry Experts
• Live, Real-World Projects
• 100% Placement Support
• Potential Interview Training
• Resume-Building Activities

Career Opportunities After Deep Learning:

• Deep Learning Engineer
• AI/ML Engineer
• Data Scientist
• Computer Vision Engineer
• Natural Language Processing
• AI Research Scientist
• Healthcare AI Specialist
• Robotics & Autonomous

Essential s Skills you will Develop Deep Learning:

• Neural Network Architecture Design
• Model Training & Optimization
• Programming with Python & Libraries
• Computer Vision Techniques
• Natural Language Processing

Tools Covered:

• Tens or Flow – Google’s open-source framework for building and training neural networks.
• Kera – High-level API built on Tens or Flow, user-friendly for beginners.
• Torch – Facebook’s dynamic framework, popular in research and production

Syllabus:

Module 1: Introduction to Deep Learning  History and evolution of AI and Deep Learning Difference between ML and DL Applications of Deep Learning (Vision, NLP, Healthcare, etc.) Overview of popular DL libraries: TensorFlow, Keras, PyTorch.

Module 2: Mathematics for Deep Learning Linear Algebra essentials vectors, matrices, dot product  Calculus basics gradients, partial derivatives Probability and statistics for DL Loss functions and optimization overview.

Module 3: Neural Networks Basics Biological inspiration and perceptron Architecture of a Neural Network Forward and backward propagation Activation functions ReLU, Sigmoid, Tanh, Softmax.

Module 4: Training Deep Neural Networks Cost functions and gradient descent Backpropagation algorithm Overfitting and underfitting Regularization techniques (L1, L2, Dropout) Hyperparameter tuning.

Module 5: Convolutional Neural Networks (CNNs) Introduction to image processing Convolution, pooling, and padding CNN architectures: LeNet, AlexNet, VGG, ResNet Applications in image classification and object detection.

Module 6: Recurrent Neural Networks (RNNs) Sequence modeling and time-series data Architecture of RNNs and vanishing gradient problem LSTM and GRU networks Applications in NLP and forecasting.

Module 7: Advanced Architectures and Techniques Autoencoders and Variational Autoencoders (VAEs) Generative Adversarial Networks (GANs) Transformer models and Self-Attention BERT and GPT overview.

Module 8: Deep Learning Frameworks and Tools Installing and setting up TensorFlow and PyTorch Writing models from scratch Using pre-trained models and transfer learning Introduction to ONNX, Keras Tuner.

Module 9: Real-World Applications & Case Studies Computer Vision (face recognition, OCR) Natural Language Processing (sentiment analysis, translation) Healthcare (disease prediction, diagnostics) Autonomous systems robotics, self-driving cars.

Module 10: Capstone Project and Deployment Selecting and scoping a project Model evaluation and metrics Model optimization and quantization Deploying deep learning models (Flask, FastAPI, Streamlit) Using cloud platforms Google Colab, AWS, Azure.

Industry Projects:

• Object Detection for Smart Surveillance
• Fraud Detection Using Deep Neural Networks
• Autonomous Vehicle Lane Detection
• Voice Recognition and Speech-to-Text
• Stock Price Prediction Using LSTM

Who is this program for?

• Engineering and Computer Science Students
• Data Science and Analytics Professionals
• Software Developers and IT Professionals
• Researchers and Academics
• Startup Founders and Tech Entrepreneurs

 How To Apply:

Mobile: 9100348679

Email: coursedivine@gmail.com