AI: Deep Learning with TensorFlow
Channel the power of deep learning with Google's TensorFlow!
Deep learning is the intersection of statistics, artificial intelligence, and data to build accurate models and TensorFlow is one of the newest and most comprehensive libraries for implementing deep learning. With deep learning going mainstream, making sense of data and getting accurate results using deep networks is possible. This course is your guide to exploring the possibilities with deep learning; it will enable you to understand data like never before. With the efficiency and simplicity of TensorFlow, you will be able to process your data and gain insights that will change how you look at data.
In this course, you will learn:
- Set up your computing environment and install TensorFlow
- Build simple TensorFlow graphs for everyday computations
- Apply logistic regression for classification with TensorFlow
- Design and train a multilayer neural network with TensorFlow
- Understand intuitively convolutional neural networks for image recognition
- Bootstrap a neural network from simple to more accurate models
- See how to use TensorFlow with other types of networks
- Program networks with SciKit-Flow, a high-level interface to TensorFlow
With this course, you will dig your teeth deeper into the hidden layers of abstraction using raw data. This course will offer you various complex algorithms for deep learning and various examples that use these deep neural networks. You will also learn how to train your machine to craft new features to make sense of deeper layers of data. During the course, you will come across topics such as logistic regression, convolutional neural networks, recurrent neural networks, training deep networks, high level interfaces, and more.
With the help of novel practical examples, you will become an ace at advanced multilayer networks, image recognition, and beyond.
This course is your go-to guide to become a deep learning expert in your organization. We evaluate common and not-so-common deep neural networks with the help of insightful examples that you can relate to, and show how they can be exploited in the real world with complex raw data.
StartLecture 11 Convolutional Layer Motivation (5:04)
StartLecture 12 Convolutional Layer Application (6:55)
StartLecture 13 Pooling Layer Motivation (3:49)
StartLecture 14 Pooling Layer Application (4:07)
StartLecture 15 Deep CNN (6:19)
StartLecture 16 Deeper CNN (3:56)
StartLecture 17 Wrapping Up Deep CNN (4:40)
Bill Gates Says These Are the Jobs He Would Drop Out of College for Today
If he were to go into computer sciences today, Gates said, the area that he thought had the most potential was artificial intelligence. Gates brought up a recent victory by Google DeepMind over the top player in the world at Go, a game some predicted a computer could never master. He called it a remarkable achievement that signaled there is more to come in advancement in artificial intelligence. And he said the research being done in the field now is "profound" and on the verge of making new breakthroughs. "The ability for artificial agents to read and understand material is going to be phenomenal," says Gates. "Anything connected with that would be an exciting lifetime career."