Free Course to Learn Neural Networks and Deep Learning

Neural Networks and Deep Learning

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What Will You Learn?

You will learn the foundational concepts of neural networks and deep learning, understand the key technological trends in deep learning, build and apply fully connected deep neural networks, implement efficient (vectorized) neural networks, identify crucial parameters in neural network architecture, and successfully apply deep learning principles to your own applications. This course serves as a gateway to comprehending the capabilities and challenges of deep learning, providing you with the knowledge and skills to contribute to cutting-edge AI technology, advance your technical career, and confidently navigate the world of machine learning.

About This Course

Provider: Coursera
Format: Online
Duration: 24 hours to complete [Approx]
Target Audience: Intermediate
Learning Objectives: By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks
Course Prerequisites: Intermediate Python skills: basic programming, understanding of for loops, if/else statements, data structures, A basic grasp of linear algebra & ML
Assessment and Certification: Earn a Certificate upon completion from the relevant Provider
Instructor: DeepLearning.AI
Key Topics: Artificial Neural Network, Backpropagation, Python Programming, Deep Learning, Neural Network Architecture
Topic Covered: 
  1. - Welcome
  2. - What is a Neural Network?
  3. - Supervised Learning with Neural Networks
  4. - Why is Deep Learning taking off?
  5. - Binary Classification
  6. - Logistic Regression
  7. - Logistic Regression Cost Function
  8. - Gradient Descent
  9. - Derivatives
  10. - More Derivative Examples
  11. - Computation Graph
  12. - Derivatives with a Computation Graph
  13. - Logistic Regression Gradient Descent
  14. - Gradient Descent on m Examples
  15. - Vectorization
  16. - More Vectorization Examples
  17. - Vectorizing Logistic Regression
  18. - Vectorizing Logistic Regression's Gradient Output
  19. - Broadcasting in Python
  20. - A Note on Python/Numpy Vectors
  21. - Quick tour of Jupyter/iPython Notebooks
  22. - Neural Networks Overview
  23. - Neural Network Representation
  24. - Computing a Neural Network's Output
  25. - Vectorizing Across Multiple Examples
  26. - Explanation for Vectorized Implementation
  27. - Activation Functions
  28. - Why do you need Non-Linear Activation Functions?
  29. - Derivatives of Activation Functions
  30. - Gradient Descent for Neural Networks
  31. - Random Initialization
  32. - Deep L-layer Neural Network
  33. - Forward Propagation in a Deep Network
  34. - Getting your Matrix Dimensions Right
  35. - Why Deep Representations?
  36. - Building Blocks of Deep Neural Networks
  37. - Forward and Backward Propagation
  38. - Parameters vs Hyperparameters
  39. - What does this have to do with the brain?

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