Deep Learning

Module 1: Introduction to Neural Networks

• Basics of neurons and perceptrons
• Multilayer perceptron and backpropagation
• Activation functions (ReLU, Sigmoid, Softmax)

Module 2: Building Deep Learning Environment

• Setting up Python and frameworks
• TensorFlow and Keras introduction

Module 3: Convolutional Neural Networks (CNN)

• CNN architecture and components
• Pooling layers, flattening
• Applications in image classification

Module 4: Recurrent Neural Networks (RNN) and LSTM

• Sequence modeling and temporal data
• Architecture and training of RNN and LSTM
• Applications in text and speech

Module 5: Advanced Architectures and Techniques

• Transfer learning, data augmentation
• Generative models and autoencoders
• Optimization techniques and regularization

Module 6: Natural Language Processing (NLP)

• Text preprocessing and embedding layers
• Sequence to sequence models and attention
• Transformer models (BERT, GPT)

Module 7: Computer Vision Applications

• Object detection and segmentation
• Image generation

Module 8: Deployment and MLOps

• Model saving, serving, and deployment on cloud
• Monitoring and maintaining deep learning pipelines

Module 9: Capstone Project

• Real-world problem solved with end-to-end deep learning pipeline

Experience in the Industry Gain knowledge from experts with practical Deep Learning project experience in a variety of sectors.

Backgrounds at the Top Prominent corporations such as HCL, TCS, Accenture, and Cognizant employ trainers.

Clear & Effective Teaching Excellent communication and real-world examples simplify complex subjects.

Hands-On Learning Focus Students can apply their abilities in real-world situations with the use of case studies and real-time projects.

Up-to-Date Knowledge Trainers stay current with the latest tools, techniques, and best practices.