OBERON GLOBAL SOLUTIONS PVT LTD

SYLLABUS - DEEP LEARNING TRAINING

Foundations of Deep Learning:

1. Introduction to Machine Learning:

• Basic concepts of supervised and unsupervised learning.

• Overview of regression and classification.

2. Mathematics for Machine Learning:

• Linear algebra, calculus, and probability.

• Understanding mathematical concepts behind neural networks.

3. Introduction to Neural Networks:

• Perceptrons, activation functions, and basic neural network architectures.

• Backpropagation algorithm.

Intermediate Deep Learning:

4. Advanced Neural Network Architectures:

• Convolutional Neural Networks (CNNs) for image processing.

• Recurrent Neural Networks (RNNs) for sequential data.

5. Training Deep Networks:

• Optimization techniques.

• Regularization methods.

6. Transfer Learning:

• Leveraging pre-trained models for new tasks.

• Fine-tuning and feature extraction.

Advanced Topics:

7. Generative Models:

• Introduction to Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).

8. Natural Language Processing (NLP) with Deep Learning:

• Word embeddings.

• Sequence-to-sequence models for language translation.

9. Reinforcement Learning:

• Basics of reinforcement learning and its applications.

Applications and Case Studies:

10. Deep Learning for Computer Vision:

• Object detection, image segmentation, etc.

11. Deep Learning for Natural Language Processing:

• Sentiment analysis, text generation, etc.

12. Industry Applications:

• Case studies and real-world applications in various industries.

Projects:

13. Hands-on Projects:

• Application of deep learning techniques to real-world problems.

• Building and training deep learning models.

Ethical and Social Implications:

14. Ethical Considerations:

• Discussions on bias, fairness, and responsible AI.

15. Future Trends:

• Exploring emerging trends in deep learning.

Capstone Project:

16. Capstone Project:

• A comprehensive project that integrates the knowledge gained throughout the course.

Prerequisites:

• Proficiency in programming languages such as Python.

• Basic understanding of linear algebra, calculus, and statistics.

• Familiarity with machine learning concepts is beneficial.

Tools and Frameworks:

• TensorFlow or PyTorch for building deep learning models.

• Jupyter Notebooks for hands-on coding.

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