Using Deep Structured Learning Or Deep Machine Video
But what is a Neural Network? - Deep learning, chapter 1 Using Deep Structured Learning Or Deep Machine.Deep Learning is transforming multiple industries.
The 5 most commonly used DL models:
This five-course specialization will help you understand Deep Learning fundamentals, apply them, and build a career in AI. Be able to explain the major trends driving the rise of deep learning, and understand where and how it is applied today. Learn to set up a machine learning problem with a neural network mindset. Learn to use vectorization to speed up your models. Learn to build a neural network with one hidden layer, using forward propagation and backpropagation.
Understand the key computations underlying deep learning, use Machlne to build and train deep neural networks, and apply it to computer vision. Learn to implement the foundational layers of CNNs pooling, convolutions and to stack them properly in a deep network to solve multi-class image classification problems. Learn about the practical tricks and methods used in deep CNNs straight from the research papers.
Learn how to apply your knowledge of CNNs to one of the toughest but hottest field of computer vision: Object detection. Discover how CNNs can be applied to multiple fields, including art generation and face recognition. Implement your own algorithm to generate art and recognize faces!
Learn about recurrent neural networks. This type of model has been proven to perform extremely well on temporal data. Natural language processing with deep learning is an important combination.
Overview of the solution
Using word vector representations and embedding layers you can train recurrent neural networks with outstanding performances in a wide variety of industries. Examples of applications are sentiment analysis, named entity recognition and machine translation. Sequence models can be augmented using an attention mechanism. This algorithm will help your model understand where it should focus its attention given a sequence of inputs. This week, you will also learn about speech recognition and how to deal with audio data. I felt like a superhero after this course. I can say neural networks are less of a black box for a lot of us after taking the course. Lecturer of Computer Science at Stanford University, deeplearning.
The workflow
Alternatively, you can enroll in individual courses. Enroll Now. Syllabus Week 1: Introduction to deep learning Be able to explain the major trends driving the rise of deep learning, and understand where and how it is applied today. Week 2: Neural Networks Basics Learn to set up a machine learning problem with a neural network mindset. Week 3: Shallow neural networks Learn to build a neural network with one hidden layer, using forward propagation and backpropagation. Week 4: Deep Neural Networks Understand the key computations underlying deep learning, use them to build and train deep neural networks, and apply it to computer vision. Week 1: Foundations of Convolutional Neural Networks Learn to implement the foundational layers of CNNs pooling, convolutions and to stack them properly in a deep network to solve multi-class image classification problems.
Week 2: Deep convolutional models: Using Deep Structured Learning Or Deep Machine studies Learn about the practical tricks and methods used in deep CNNs straight from the research papers.
Week 3: Object detection Learn how to apply your knowledge of CNNs to one of the toughest but hottest field of computer vision: Object detection. Week 1: Recurrent Neural Networks Learn about recurrent neural networks. Chris Morrow Sr. Program Instructors.]
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