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Deep Learning Basics
YOUR PATHWAY TO SUCCESS
This 5-day course provides a foundational understanding of deep learning, a powerful subfield of machine learning that has revolutionized artificial intelligence. Participants will explore the core concepts of neural networks, including different types of layers, activation functions, and optimization algorithms.
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Course Duration
5 Days
Course Details
This 5-day course provides a foundational understanding of deep learning, a powerful subfield of machine learning that has revolutionized artificial intelligence. Participants will explore the core concepts of neural networks, including different types of layers, activation functions, and optimization algorithms. The course covers the fundamentals of training deep learning models, evaluating their performance, and applying them to various tasks such as image classification and natural language processing. Through hands-on exercises using deep learning frameworks like TensorFlow or PyTorch, learners will gain practical experience in building and training deep learning models.
This course focuses on building intuition about how deep learning models work and how to choose the right architecture and parameters for a given task. It covers both supervised and unsupervised learning techniques in the context of deep learning. The course emphasizes practical application and includes hands-on projects where participants will build and train their own deep learning models. By the end of this course, participants will be able to design, train, and evaluate basic deep learning models and have the foundation for further study in this rapidly evolving field.
By the end of this course, learners will be able to:
- Understand the basic concepts of neural networks.
- Build and train simple deep learning models.
- Apply deep learning to image classification and other tasks.
- Evaluate the performance of deep learning models.
- Use deep learning frameworks like TensorFlow or PyTorch.
- Data scientists, machine learning engineers, and AI researchers.
- Individuals who want to learn about deep learning and its applications.
- Anyone who wants to build and train deep learning models.
Course Outline
5 days Course
- Introduction to Neural Networks:
- What is deep learning?
- Perceptrons and multi-layer perceptrons.
- Activation functions and backpropagation.
- Practical exercise: Building a simple neural network.
- Deep Learning Architectures:
- Convolutional Neural Networks (CNNs) for image processing.
- Recurrent Neural Networks (RNNs) for sequential data.
- Autoencoders for unsupervised learning.
- Practical exercise: Building a CNN for image classification.
- Training Deep Learning Models:
- Optimization algorithms: Gradient descent, Adam.
- Regularization techniques: Dropout, L1/L2 regularization.
- Data augmentation.
- Practical exercise: Training a deep learning model on a real-world dataset.
- Training Deep Learning Models:
- Evaluating Deep Learning Models:
- Performance metrics: Accuracy, precision, recall, F1-score.
- Cross-validation and hyperparameter tuning.
- Practical exercise: Evaluating the performance of a deep learning model.
- Evaluating Deep Learning Models:
- Deep Learning Applications:
- Image recognition and object detection.
- Natural language processing.
- Other applications of deep learning.
- Practical exercise: Applying deep learning to a specific task.
- Deep Learning Applications:
