Welcome to the DeepLearning.AI Deep Learning Specialization by Andrew Ng repository! This repository contains all my coursework, assignments, and projects completed during the Deep Learning Specialization offered by DeepLearning.AI on Coursera, taught by Andrew Ng.
The Deep Learning Specialization is a foundational program that will help you understand deep learning, its applications, and build neural network models. The specialization consists of five courses:
This course provides an introduction to neural networks and deep learning. You will learn about:
- The foundations of deep learning.
- The key parameters in a neural network's architecture.
- How to set up a machine learning problem with a neural network mindset.
- How to implement a neural network using Python and NumPy.
In this course, you will delve deeper into the mechanics of deep learning and learn techniques to improve your models. Topics include:
- Practical aspects of deep learning.
- Setting up your machine learning project with proper train/dev/test sets.
- Hyperparameter tuning, batch normalization, and dropout regularization.
- Implementing optimization algorithms such as mini-batch gradient descent, RMSprop, and Adam.
This course focuses on how to build a successful machine learning project. You will learn:
- How to structure machine learning projects.
- Strategies for reducing error in machine learning systems.
- The importance of orthogonalization.
- Establishing a baseline level of performance and setting up a productive machine learning environment.
This course covers convolutional neural networks (CNNs) and their applications. Topics include:
- The basics of CNNs and how they work.
- Building and training CNNs for image recognition and computer vision tasks.
- Advanced architectures such as ResNets, Inception networks, and more.
- Practical applications of CNNs including object detection and neural style transfer.
In this course, you will explore sequence models and their applications. You will learn about:
- Recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and gated recurrent units (GRUs).
- How to apply sequence models to natural language processing and other tasks involving sequential data.
- Building and training sequence models for tasks such as language modeling, speech recognition, and machine translation.
- Attention mechanisms and how they enhance the performance of sequence models.
I want to thank Andrew Ng and the entire DeepLearning.AI team for creating this incredible specialization and providing a strong foundation in deep learning.
Note: This repository is for educational purposes only.
