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Deep Dive into Python Machine Learning (2016)


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Deep Dive into Python Machine Learning (2016)

Deep Dive into Python Machine Learning (2016)

MP4 | AVC 116kbps | English | 1280x720 | 25fps | 1h 45mins | AAC stereo 141kbps | 2.64 GB
Genre: Video Training

Deep learning is currently one of the best providers of solutions regarding problems in image recognition, speech recognition, object recognition, and natural language with its increasing number of libraries that are available in Python. The aim of deep learning is to develop deep neural networks by increasing and improving the number of training layers for each network, so that a machine learns more about the data until it’s as accurate as possible. Developers can avail the techniques provided by deep learning to accomplish complex machine learning tasks, and train AI networks to develop deep levels of perceptual recognition.

Deep learning is the next step to machine learning with a more advanced implementation. Currently, it’s not established as an industry standard, but is heading in that direction and brings a strong promise of being a game changer when dealing with raw unstructured data. Deep learning is currently one of the best providers of solutions regarding problems in image recognition, speech recognition, object recognition, and natural language processing. Developers can avail the benefits of building AI programs that, instead of using hand coded rules, learn from examples how to solve complicated tasks. With deep learning being used by many data scientists, deeper neural networks are evaluated for accurate results.

This course takes you from basic calculus knowledge to understanding backpropagation and its application for training in neural networks for deep learning and understand automatic differentiation. Through the course, we will cover thorough training in convolutional, recurrent neural networks and build up the theory that focuses on supervised learning and integrate into your product offerings such as search, image recognition, and object processing. Also, we will examine the performance of the sentimental analysis model and will conclude with the introduction of Tensorflow.

By the end of this course, you can start working with deep learning right away. This course will make you confident about its implementation in your current work as well as further research.

Style and Approach
An easy-to-follow and structured video tutorial with practical examples and coding with IPython notebooks to help you get to grips with each and every aspect of deep learning.

Deep Dive into Python Machine Learning (2016)

Deep Dive into Python Machine Learning (2016)

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  1. Python深度机器学习 随着Python不但增加的类库,深度学习是当前对于图像识别、语音识别、对象识别以及自然语言最好的解决方案之一。深度学习的目的是要通过增加和改进每个网络的培训层数量,开发出深度的神经网络,这样一台机器能够更多地学习数据,直到其尽可能地准确。开发者可以利用深度学习的技术完成复杂的机器学习任务,训练AI网络开发出深层次的感性认知。 本教程会带你从基础的微积分知识学起直到反向传播,以及其用于在神经网络中的用于深度学习和理解自动差异化的应用。在本教程中,我们将学习卷积、循环神经网络,并会逐步学习监督学习的理论并将其整合到你的产品中,如搜索、图像识别以及对象处理。同样,我们还会学习情感分析的性能模型,并会以Tensorflow的介绍作为总结。 学习完本教程后,你将能自信地处理深度学习中的问题了。
    wilde(特殊组-翻译)2年前 (2017-01-05)