Introduction to Deep Learning

开始时间: 02/22/2020 持续时间: Unknown

所在平台: Coursera

课程类别: 其他类别

大学或机构: CourseraNew



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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. Learners will use these building blocks to define complex modern architectures in TensorFlow and Keras frameworks. In the course project learner will implement deep neural network for the task of image captioning which solves the problem of giving a text description for an input image. The prerequisites for this course are: 1) Basic knowledge of Python. 2) Basic linear algebra and probability. Please note that this is an advanced course and we assume basic knowledge of machine learning. You should understand: 1) Linear regression: mean squared error, analytical solution. 2) Logistic regression: model, cross-entropy loss, class probability estimation. 3) Gradient descent for linear models. Derivatives of MSE and cross-entropy loss functions. 4) The problem of overfitting. 5) Regularization for linear models. Do you have technical problems? Write to us:

深度学习简介:本课程的目的是使学习者对现代神经网络及其在计算机视觉和自然语言理解中的应用有基本的了解。本课程从线性模型的回顾和对优化深度神经网络至关重要的随机优化方法的讨论开始。学习者将研究神经网络的所有流行构建模块,包括完全连接的层,卷积层和循环层。 学习者将使用这些构建块在TensorFlow和Keras框架中定义复杂的现代架构。在课程中,项目学习者将实现深度神经网络来完成图像字幕的任务,从而解决为输入图像提供文本描述的问题。 本课程的前提条件是: 1)Python的基本知识。 2)基本线性代数和概率。 请注意,这是一门高级课程,我们假设您具备机器学习的基础知识。您应该了解: 1)线性回归:均方误差,解析解。 2)Logistic回归:模型,交叉熵损失,类概率估计。 3)线性模型的梯度下降。 MSE的导数和交叉熵损失函数。 4)过拟合的问题。 5)线性模型的正则化。 你有技术上的问题吗?写信给我们


Welcome to the "Introduction to Deep Learning" course! In the first week you'll learn about linear models and stochatic optimization methods. Linear models are basic building blocks for many deep architectures, and stochastic optimization is used to learn every model that we'll discuss in our course.





The goal of this course is to give learners basic understanding of modern neural networks and their