Course Summary
Learn the basics of Deep Learning and Artificial Intelligence in this introductory course. Explore the fundamental concepts and techniques used in modern AI applications.Key Learning Points
- Get hands-on experience using popular Deep Learning frameworks like Tensorflow and Keras.
- Learn how to build and train neural networks for image and speech recognition.
- Discover how Deep Learning is being used in industries like healthcare, finance, and self-driving cars.
Related Topics for further study
- Neural Networks
- Deep Learning Frameworks
- Computer Vision
- Natural Language Processing
- Artificial Intelligence Applications
Learning Outcomes
- Understand the basics of Deep Learning and Artificial Intelligence
- Gain hands-on experience using popular Deep Learning frameworks
- Build and train neural networks for image and speech recognition
Prerequisites or good to have knowledge before taking this course
- Basic programming knowledge (Python recommended)
- High school math (Algebra and Calculus)
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
- Video lectures
Similar Courses
- Applied Data Science with Python
- Applied AI with DeepLearning
- Introduction to Machine Learning
Related Education Paths
Notable People in This Field
- Professor at New York University, Chief AI Scientist at Facebook
- Professor at the University of Toronto, Chief Scientific Adviser at the Vector Institute
Related Books
Description
The goal of this course is to give learners basic understanding of modern neural networks and their applications in computer vision and natural language understanding. The course starts with a recap of linear models and discussion of stochastic optimization methods that are crucial for training deep neural networks. Learners will study all popular building blocks of neural networks including fully connected layers, convolutional and recurrent layers.
Outline
- Introduction to optimization
- About the University
- Welcome to AML specialization!
- Course intro
- Linear regression
- Linear classification
- Gradient descent
- Overfitting problem and model validation
- Model regularization
- Stochastic gradient descent
- Gradient descent extensions
- About University
- Rules on the academic integrity in the course
- Welcome!
- Hardware for the course
- Linear models
- Overfitting and regularization
- Introduction to neural networks
- Multilayer perceptron (MLP)
- Chain rule
- Backpropagation
- Efficient MLP implementation
- Other matrix derivatives
- What is TensorFlow
- Our first model in TensorFlow
- What Deep Learning is and is not
- Deep learning as a language
- Optional reading on matrix derivatives
- TensorFlow reading
- Keras reading
- Multilayer perceptron
- Matrix derivatives
- Deep Learning for images
- Motivation for convolutional layers
- Our first CNN architecture
- Training tips and tricks for deep CNNs
- Overview of modern CNN architectures
- Learning new tasks with pre-trained CNNs
- A glimpse of other Computer Vision tasks
- Convolutions and pooling
- Unsupervised representation learning
- Unsupervised learning: what it is and why bother
- Autoencoders 101
- Autoencoder applications
- Autoencoder applications: image generation, data visualization & more
- Natural language processing primer
- Word embeddings
- Generative models 101
- Generative Adversarial Networks
- Applications of adversarial approach
- Word embeddings
- Deep learning for sequences
- Motivation for recurrent layers
- Simple RNN and Backpropagation
- The training of RNNs is not that easy
- Dealing with vanishing and exploding gradients
- Modern RNNs: LSTM and GRU
- Practical use cases for RNNs
- RNN and Backpropagation
- Modern RNNs
- How to use RNNs
- Final Project
Summary of User Reviews
The Intro to Deep Learning course on Coursera has received positive reviews from users. Many users found the course to be informative and well-structured, making it easy to follow along and understand the material. Overall, users highly recommend this course.Key Aspect Users Liked About This Course
The course is well-structured and easy to follow.Pros from User Reviews
- The content is informative and well-explained.
- The instructors are knowledgeable and engaging.
- The course provides hands-on experience with real-world applications.
- The course is a great introduction to deep learning for beginners.
Cons from User Reviews
- The course may be too basic for those with prior experience in deep learning.
- Some users found the pace of the course to be too slow.
- The course doesn't cover advanced topics in deep learning.
- Some users found the programming assignments to be challenging.
Evgeny Sokolov
4.5 Raiting
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