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Deep Learning Projects – Handwritten Digit Recognition Using Neural Network

Deep Learning Projects – Handwritten Digit Recognition Using Neural Network

₦45,500.00

Welcome to the “Deep Learning Projects – Handwritten Digit Recognition Using Neural Network” course! This program focuses on developing practical skills in building neural network models for handwritten digit recognition, a fundamental task in the field of deep learning and computer vision. Participants will learn how to preprocess image data, design neural network architectures, and train models using popular deep learning frameworks.

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Description

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Welcome to the “Deep Learning Projects – Handwritten Digit Recognition Using Neural Network” course! This program focuses on developing practical skills in building neural network models for handwritten digit recognition, a fundamental task in the field of deep learning and computer vision. Participants will learn how to preprocess image data, design neural network architectures, and train models using popular deep learning frameworks.

Main Course Features:

  • Comprehensive introduction to neural networks and their applications in image recognition
  • Step-by-step tutorials on building and training neural network models for handwritten digit recognition
  • Hands-on coding projects using Python and deep learning libraries such as TensorFlow and Keras
  • Exploration of different neural network architectures, including fully connected networks and convolutional neural networks (CNNs)
  • Practical guidance on data preprocessing techniques, feature extraction, and model evaluation
  • Access to curated datasets of handwritten digits for training and testing neural network models
  • Expert insights and best practices for optimizing neural network performance and avoiding common pitfalls
  • Opportunities for collaborative learning through online forums, group projects, and code reviews

Who Should Take This Course:

  • Data scientists, machine learning engineers, and software developers interested in deep learning and computer vision
  • Students and researchers studying artificial intelligence, machine learning, or computer vision
  • Professionals working in fields such as finance, healthcare, and logistics, where handwriting recognition is applicable
  • Anyone interested in gaining practical experience in building neural network models for image recognition tasks

Learning Outcomes:

  • Develop a strong understanding of neural network architectures and their components
  • Acquire practical skills in preprocessing image data and designing neural network models
  • Gain proficiency in training and evaluating neural network models for handwritten digit recognition
  • Learn how to optimize neural network performance through hyperparameter tuning and regularization techniques
  • Apply neural network models to real-world projects, such as digit classification and OCR (optical character recognition)
  • Explore advanced topics in deep learning, including transfer learning and model interpretability
  • Build a portfolio of projects showcasing neural network applications in image recognition
  • Demonstrate competency in implementing neural network solutions for handwritten digit recognition through hands-on projects and assessments.

Certification

Once you’ve successfully completed your course, you will immediately be sent a digital certificate. Also, you can have your printed certificate delivered by post (shipping cost £3.99). All of our courses are fully accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field. Our certifications have no expiry dates, although we do recommend that you renew them every 12 months.

Assessment

At the end of the Course, there will be an online assessment, which you will need to pass to complete the course. Answers are marked instantly and automatically, allowing you to know straight away whether you have passed. If you haven’t, there’s no limit on the number of times you can take the final exam. All this is included in the one-time fee you paid for the course itself.