Learn the fundamentals of deep learning and its applications in real-world scenarios.
Overview
Deep learning is a subset of machine learning that focuses on algorithms inspired by the structure and function of the brain, known as artificial neural networks. This course provides a comprehensive introduction to the principles and techniques of deep learning.
Participants will explore various architectures, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), and learn how to implement them using popular frameworks like TensorFlow and PyTorch. By the end of this course, learners will be equipped with the knowledge to tackle real-world problems using deep learning.
Features
Understand the core concepts of deep learning
Implement neural networks using TensorFlow and PyTorch
Apply deep learning techniques to solve real-world problems
Target audiences
Aspiring data scientists
Software engineers
Machine learning enthusiasts
Students in computer science
Requirements
Basic understanding of Python
Familiarity with machine learning concepts
Mathematics knowledge (linear algebra, calculus)
Curriculum
5 sections
11 lectures 12 hours
Introduction to Deep Learning
2 lectures
What is Deep Learning?
Locked
Neural Networks Basics
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Deep Learning Frameworks
2 lectures
Getting Started with TensorFlow
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Using PyTorch for Deep Learning
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Convolutional Neural Networks
2 lectures
Introduction to CNNs
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Building a CNN from Scratch
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Recurrent Neural Networks
2 lectures
Understanding RNNs
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Implementing RNNs with PyTorch
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Real-World Applications
3 lectures
Deep Learning in Natural Language Processing
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Deep Learning for Image Recognition
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Deep Learning Knowledge Check
Locked
Frequently Asked Questions
01.What is deep learning?
Deep learning is a type of machine learning that uses neural networks to model complex patterns in data.
02.Do I need prior experience?
A basic understanding of Python and machine learning concepts is recommended.
