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Deep Learning

Track :

Programming

Lessons no : 66

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What will you learn in this course?
  • Master designing deep neural networks for complex data analysis and pattern recognition in real-world applications
  • Implement deep learning algorithms for computer vision, speech recognition, and natural language processing tasks
  • Apply deep learning techniques to develop virtual assistants, face recognition systems, and autonomous vehicle vision systems
  • Optimize deep learning models for accuracy, efficiency, and scalability in large-scale data environments
  • Utilize popular deep learning frameworks like TensorFlow and PyTorch for model development and deployment
  • Identify and mitigate overfitting, underfitting, and training challenges in deep neural networks
  • Analyze structured and unstructured data to enhance deep learning model performance and insights
  • Develop deep learning solutions for fraud detection, money laundering prevention, and financial security
  • Integrate deep learning models into real-world applications such as driverless cars and security systems
  • Evaluate the ethical considerations and biases in deep learning models for responsible AI deployment
  • Design transfer learning and fine-tuning strategies to improve model accuracy with limited data
  • Implement data preprocessing, augmentation, and feature extraction techniques for deep learning projects
  • Troubleshoot and debug deep learning models to ensure robustness and reliability in production environments
  • Stay updated with the latest advancements and research trends in deep learning and artificial intelligence

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Lessons | 66
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shishir ligadi

good 2025-04-20

Aditya Patil

it was good 2025-04-20

Disha Rajendra Pawar

Wow 2025-04-20

Khushi Choudhary

it was good 2025-04-20

Pratik Waghmare

it was good 2025-04-20

Om Alande

cehcgeljkh 2025-04-18

Diksha Patil

good 2025-04-18

Shrawani Malavade

good 2025-04-18

Zoubeir Avşar

This course about Deep Learning is very interesting 2024-03-25

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Deep learning utilizes both structured and unstructured data for training. Practical examples of deep learning are Virtual assistants, vision for driverless cars, money laundering, face recognition and many more .