Deep Learning

Build and deploy sophisticated AI models that excel in image recognition, language processing, and predictive analytics.

Duration

2 Days

Language

English

Delivery Mode

Online/Hybrid/Face-to-Face

Course Overview

Learning Objectives

  • Analyze the structural components and functional mechanics of multi-layered neural networks.
  • Implement optimization algorithms and regularization techniques to enhance model accuracy and efficiency.
  • Construct Convolutional Neural Networks (CNNs) for sophisticated image classification and object detection tasks.
  • Develop Recurrent Neural Networks (RNNs) and LSTMs to process sequential data and natural language.
  • Utilize leading frameworks like PyTorch and TensorFlow to build, train, and evaluate deep learning architectures.
  • Evaluate strategies for model interpretability and successful deployment within production environments.

Course Outline

Day 01
  • Foundations of Neural Architectures: Feedforward Networks and Backpropagation.
  • Deep Learning Optimization: Activation Functions, Loss Calculation, and Training Refinement.
  • Visual Intelligence: Designing and Implementing Convolutional Neural Networks (CNNs).
  • Sequence Modeling: Exploring RNNs, GRUs, and LSTMs for Time-Series and Text.
  • Generative Modeling: Principles and Applications of Generative Adversarial Networks (GANs).
  • Operationalizing AI: Model Interpretation, Troubleshooting, and Deployment Frameworks.

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