深度学习:从无到有 Deep Learning : From Scratch To The Depth

深度学习的完整课程

你会学到什么
使用深度学习对图像、数据和情感进行分类
使用线性回归、多项式回归和多元回归进行预测
用MatPlotLib和Seaborn实现数据可视化
清理输入数据以移除异常值
python的高级用法

MP4 |视频:h264，1280×720 |音频:AAC，44.1 KHz
语言：英语+中英文字幕（云桥网络 机译）|大小解压后:4.66 GB |时长:8小时43分


要求
只是对编程有个基本的了解。剩下的交给我们吧！！

描述
为了避开个人电脑这种僵硬的想法，复杂多样的深层大脑网络被构建并用作学习的基础。深度学习模型不是作为顺序程序工作，而是利用由中枢连接的网络工作——就像人类大脑如何利用大量相关神经元工作一样。它是设计进步、最佳实践和假设的融合，赋予了大量以前无法想象的精明应用。尽管它是一台可编程的机器，但深度学习框架不像普通电脑那样处理信息。一个孤独而深刻的大脑网络由不同的层次组成——层次越多，结果就越精确。尽管如此，他们不是独立工作或交替工作，而是在彼此工作的基础上扩展。虽然大脑网络的工作方式会有所波动，而且不是每一个都以直线设计的方式处理信息，但它们都有相对的结构。‌Rather再一次用信息的粗糙信息工作，鉴于过去层的预测和分类，每一层用稍微更精炼和简化的再现工作。除了信息和结果层之外，机器在这些层中所做的工作大部分都被掩盖了，这些层也被称为“表观层”。

目录：

第一部分:导言

第1讲深度学习简介

第2讲深度学习介绍-神经网络

第二部分:深度学习模型

第3讲深度学习模型-基础知识

第4讲深度学习模型-详细

第5讲深度学习模型——神经网络及其特征

第六讲深度学习模型——神经网络及其特征

第7讲深度学习模型——神经网络及其特征

第3部分:附加模型

第8讲附加深度学习模型-第1部分

第9讲附加深度学习模型-第2部分

第10讲附加深度学习模型——生成式对抗网络(GAN)

第4节:图书馆

第11讲深度学习平台库-基础知识

第12讲深度学习平台库-基础知识

第13讲深度学习平台库-数据图表实验室

第14讲深度学习平台库- DataGraph实验室结论

第15讲深度学习平台图书馆——the ano和Caffe

第16讲深度学习平台库–tensor flow第1部分

第17讲深度学习平台库- Theano或TensorFlow第2部分

对深度学习和数据科学感兴趣的Python开发人员、编程爱好者、希望拓宽python技能的学生

MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 4.52 GB | Duration: 8h 43m

Complete package of deep learning

What you’ll learn
Classify images, data, and sentiments using deep learning
Make predictions using linear regression, polynomial regression, and multivariate regression
Data Visualization with MatPlotLib and Seaborn
Clean your input data to remove outliers
Advanced usage of python
Requirements
Just a basic understanding of programming. We’ll take care of the rest !!
Description
To sidestep the unbending idea of PCs, complex and diverse profound brain networks are constructed and utilized as a base for learning. Rather than working as a sequential program, profound learning models work utilizing networks joined by hubs — like how the human mind utilizes a large number of associated neurons to work. It’s the blend of designing advances, best practices, and hypotheses that empowers an abundance of beforehand unimaginable savvy applications.Regardless of as yet being a programmable machine, profound learning frameworks don’t deal with information like your normal PC. A solitary profound brain network comprises various layers — the more layers there are, the more exact the outcomes. Be that as it may, rather than working independently or alternating, they expand upon one another’s work. While brain networks fluctuate by the way they work, and not every one of them process information in a straight design, they all have a comparative establishment.‌Rather than working with the crude information of the info once more, each layer works with a somewhat more refined and streamlined rendition in view of the past layer’s forecast and classification. The work the machine does inside the layers remains for the most part covered up, except for information and result layers, otherwise called the ‘apparent layers’.

Overview

Section 1: INTRODUCTION

Lecture 1 Introduction to Deep Learning

Lecture 2 Introduction to Deep Learning – neural networks

Section 2: DEEP LEARNING MODELS

Lecture 3 Deep Learning Models – basics

Lecture 4 Deep Learning Models – DETAILED

Lecture 5 Deep Learning Models – neural network and its characteristics

Lecture 6 Deep Learning Models – neural network and its characteristics

Lecture 7 Deep Learning Models – neural network and its characteristics

Section 3: ADDITIONAL MODELS

Lecture 8 Additional Deep Learning Models – Part 1

Lecture 9 Additional Deep Learning Models – Part 2

Lecture 10 Additional Deep Learning Models – Generative Adversarial Networks (GAN)

Section 4: LIBRARIES

Lecture 11 Deep Learning Platforms Libraries – basics

Lecture 12 Deep Learning Platforms Libraries – basics

Lecture 13 Deep Learning Platforms Libraries – DataGraph Lab

Lecture 14 Deep Learning Platforms Libraries – DataGraph Lab Conclusion

Lecture 15 Deep Learning Platforms Libraries – Theano and Caffe

Lecture 16 Deep Learning Platforms Libraries – TensorFlow part -1

Lecture 17 Deep Learning Platforms Libraries – Theano or TensorFlow Part – 2

Python developers curious about deep learning and data science,Programming enthusiasts,Students who wish to broaden their skillsets in python

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