If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.
The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.
WHAT YOU WILL LEARN
- Learn best practices for using TensorFlow, a popular open-source machine learning framework
- Build a basic neural network in TensorFlow
- Train a neural network for a computer vision application
- Understand how to use convolutions to improve your neural network
Artificial Intelligence (AI) is a field that has a long history but is still constantly and actively growing and changing. In this course, you'll learn the basics of modern AI as well as some of the representative applications of AI. Along the way, we also hope to excite you about the numerous applications and huge possibilities in the field of AI, which continues to expand human capability beyond our imagination.
This course will introduce you to the basics of AI. Topics include machine learning, probabilistic reasoning, robotics, computer vision, and natural language processing.
What you'll learn
- Apply gradient-based supervised machine learning methods to reinforcement learning
- Understand reinforcement learning on a technical level
- Understand the relationship between reinforcement learning and psychology
- Implement 17 different reinforcement learning algorithms
When people talk about artificial intelligence, they usually don't mean supervised and unsupervised machine learning.
- These tasks are pretty trivial compared to what we think of AIs doing - playing chess and Go, driving cars, and beating video games at a superhuman level.
- Reinforcement learning has recently become popular for doing all of that and more.
- Much like deep learning, a lot of the theory was discovered in the 70s and 80s but it hasn't been until recently that we've been able to observe first hand the amazing results that are possible.
What You Will Learn
- Learn AI Fundamentals
- Learn the essential foundations of AI: the programming tools (Python, NumPy, PyTorch), the math (calculus and linear algebra), and the key techniques of neural networks (gradient descent and backpropagation).
- Introduction to Python
- Start coding with Python, drawing upon libraries and automation scripts to solve complex problems quickly.
What is this course about? What do a web search, speech recognition, face recognition, machine translation, autonomous driving, and automatic scheduling have in common? These are all complex real-world problems, and the goal of artificial intelligence (AI) is to tackle these with rigorous mathematical tools. In this course, you will learn the foundational principles that drive these applications and practice implementing some of these systems. Specific topics include machine learning, search, game playing, Markov decision processes, constraint satisfaction, graphical models, and logic. The main goal of the course is to equip you with the tools to tackle new AI problems you might encounter in life.
Prerequisites: This course is fast-paced and covers a lot of ground, so it is important that you have a solid foundation on both the theoretical and empirical fronts.