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所在平台: Udemy |
课程主页: https://www.udemy.com/course/beginners-guide-to-deep-learning/
课程评论：没有评论
**Coursera 课程：《深度学习入门指南》内容摘要** 本课程专为**有机器学习基础的数据科学家**设计，提供对当下发展最快、最重要的机器学习子领域——深度学习——的扎实、实用的入门介绍。 对于**希望入门 Keras 框架的深度学习专家**而言，本课程是不可多得的 Keras 速成宝典。 对于**在校学习深度学习的研究生**，本课程将作为其学术教育的实用补充，帮助您深入理解深度神经网络的行为，并熟悉关键的最佳实践。 **深度学习**作为一种机器学习技术，其核心在于教会计算机通过“以身作则”的方式进行学习，这更贴近人类的学习方式。深度学习是诸如**自动驾驶汽车**（识别交通标志、区分行人与灯柱）以及**消费电子设备**（手机、平板、电视、智能音箱等的语音控制）等先进技术的核心驱动力。它之所以受到广泛关注，源于其能够取得前所未有的突破性成果。 **深度学习**是人工智能（AI）的一个分支，致力于构建能够从数据中学习并随着时间推移自主提升准确度的应用程序，而无需进行硬性编程。 在数据科学领域，**算法**是一系列统计处理步骤。在**机器学习**中，算法通过“训练”来处理海量数据，从中发现模式和特征，以便根据新数据做出决策和预测。算法的性能越好，其处理的数据越多，决策和预测的准确性也会越高。 深度学习已在多个领域取得惊人成就，例如在**医疗影像分析**方面，其疾病预测能力已达到与人类专家相当的水平。此外，**谷歌的 AlphaGo 程序**也运用了深度强化学习，成功击败了世界围棋冠军。 **课程涵盖的主题包括：** 1. **深度学习理论概念基础：** 神经元、神经网络、激活函数等。 2. **深度学习实践概念：** 张量、张量运算、梯度下降、反向传播等。 3. **用于深度学习项目构建的神经网络：** 涵盖电影评论分类、新闻分类、房价预测等实际项目。 4. **深度学习相关的机器学习概念：** 数据预处理、网络规模、Dropout（正则化技术）等。 5. **计算机视觉中的深度学习：** 卷积神经网络（CNN）。 6. **循环神经网络 (RNN)：** （预计于 2022 年 11 月 25 日更新加入）。 **注意：** 本课程将不断更新，加入大量项目和深度学习概念。 **教学大纲：** 无（未提供）。
If you're a data scientist familiar with machine learning, this course will provide you with a solid, practical introduction to deep learning, the fastest-growing and most significant subfield of machine learning. If you're a deep-learning expert looking to get started with the Keras framework, you'll find this course to be the best Keras crash course available. If you're a graduate student studying deep learning in a formal setting, you'll find this course to be a practical complement to your education, helping you build intuition around the behavior of deep neural networks and familiarizing you with key best practices.[Note: This course will be updated every weeks with tons of projects, and deep learning concepts]Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting lots of attention lately and for good reason. It's achieving results that were not possible before.Deep Learning is a branch of artificial intelligence (AI) focused on building applications that learn from data and improve their accuracy over time without being programmed to do so.In data science, an algorithm is a sequence of statistical processing steps. In machine learning, algorithms are 'trained' to find patterns and features in massive amounts of data in order to make decisions and predictions based on new data. The better the algorithm, the more accurate the decisions and predictions will become as it processes more data.Deep Learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts.Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning.Topics covered in this course:1. Building Theoretical Concept for Deep Learning: Neurons, Neural Networks, Activation Function etc2. Building Practical Concept: Tensor, Tensor Operations, Gradient Descent, Backpropagation etc3. Neural Networks in Details for Deep Learning building Projects: Movie Review Classification, Newswire classification, house price predictions.4. Machine Learning concepts for Deep Learning: Data preprocessing, Network size, dropout etc5. Deep Learning for Computer Vision: Convolution Neural Network6. Recurrent Neural Network (will be added in 25 Nov, 2022)
