Python Machine Learning Machine Learning and Deep Learning

Python Machine Learning Machine Learning and Deep Learning with Python, scikitlearn, and TensorFlow, 2nd Edition 很受推荐
 664KB
Python Machine Learning and Deep Learning with Python
20190716Python Machine Learning and Deep Learning with Python, scikitlearn and Tensorflow (StepbyStep Tutorial For Beginners)
 10.17MB
Python Machine Learning and Deep Learning with python, sklearn, tf,2nd Edition
20180905Python Machine Learning: Machine Learning and Deep Learning with Python, scikitlearn, and TensorFlow, 2nd EditionSep 20, 2017 by Sebastian Raschka and Vahid Mirjalili
 10.5MB
Machine Learning and Deep Learning with Python, scikitlearn, and TensorFlow
20180317Table of Contents Giving Computers the Ability to Learn from Data Training Simple Machine Learning Algorithms for Classification A Tour of Machine Learning Classifiers Using ScikitLearn Building Good Training Sets  Data Preprocessing Compressing Data via Dimensionality Reduction Learning Best Practices for Model Evaluation and Hyperparameter Tuning Combining Different Models for Ensemble Learning Applying Machine Learning to Sentiment Analysis Embedding a Machine Learning Model into a Web Application Predicting Continuous Target Variables with Regression Analysis Working with Unlabeled Data  Clustering Analysis Implementing a Multilayer Artificial Neural Network from Scratch Parallelizing Neural Network Training with TensorFlow Going Deeper  The Mechanics of TensorFlow Classifying Images with Deep Convolutional Neural Networks Modeling Sequential Data using Recurrent Neural Networks
 7.20MB
HandsOn Data Science and Python Machine Learning
20170815HandsOn Data Science and Python Machine Learning by Frank Kane English  31 July 2017  ISBN: 1787280748  ASIN: B072QBVXGH  420 Pages  AZW3  7.21 MB Key Features Take your first steps in the world of data science by understanding the tools and techniques of data analysis Train efficient Machine Learning models in Python using the supervised and unsupervised learning methods Learn how to use Apache Spark for processing Big Data efficiently Book Description Join Frank Kane, who worked on Amazon and IMDb's machine learning algorithms, as he guides you on your first steps into the world of data science. HandsOn Data Science and Python Machine Learning gives you the tools that you need to understand and explore the core topics in the field, and the confidence and practice to build and analyze your own machine learning models. With the help of interesting and easytofollow practical examples, Frank Kane explains potentially complex topics such as Bayesian methods and Kmeans clustering in a way that anybody can understand them. Based on Frank's successful data science course, HandsOn Data Science and Python Machine Learning empowers you to conduct data analysis and perform efficient machine learning using Python. Let Frank help you unearth the value in your data using the various data mining and data analysis techniques available in Python, and to develop efficient predictive models to predict future results. You will also learn how to perform largescale machine learning on Big Data using Apache Spark. The book covers preparing your data for analysis, training machine learning models, and visualizing the final data analysis. What you will learn Learn how to clean your data and ready it for analysis Implement the popular clustering and regression methods in Python Train efficient machine learning models using decision trees and random forests Visualize the results of your analysis using Python's Matplotlib library Use Apache Spark's MLlib package to perform
 10.89MB
Python Machine Learning Second Edition
20171214Machine Learning and Deep Learning with Python, scikitlearn, and TensorFlow Sebastian Raschka Vahid Mirjalili
 19.96MB
Python Machine Learning 2nd Edition [Sebastian Raschka]
20170922What you will learn Understand the key frameworks in data science, machine learning, and deep learning Harness the power of the latest Python open source libraries in machine learning Explore machine learning techniques using challenging realworld data Master deep neural network implementation using the TensorFlow library Learn the mechanics of classification algorithms to implement the best tool for the job Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Delve deeper into textual and social media data using sentiment analysis
 9.89MB
Python Machine Learning. Machine Learning and Deep Learning with Py, scikit
20180729Through exposure to the news and social media, you are probably aware of the fact that machine learning has become one of the most exciting technologies of our time and age. Large companies, such as Google, Facebook, Apple, Amazon, and IBM, heavily invest in machine learning research and applications for good reasons. While it may seem that machine learning has become the buzzword of our time and age, it is certainly not a fad. This exciting field opens the way to new possibilities and has become indispensable to our daily lives. This is evident in talking to the voice assistant on our smartphones, recommending the right product for our customers, preventing credit card fraud, filtering out spam from our email inboxes, detecting and diagnosing medical diseases, the list goes on and on. If you want to become a machine learning practitioner, a better problem solver, or maybe even consider a career in machine learning research, then this book is for you. However, for a novice, the theoretical concepts behind machine learning can be quite overwhelming. Many practical books have been published in recent years that will help you get started in machine learning by implementing powerful learning algorithms. Getting exposed to practical code examples and working through example applications of machine learning are a great way to dive into this field. Concrete examples help illustrate the broader concepts by putting the learned material directly into action. However, remember that with great power comes great responsibility! In addition to offering a handson experience with machine learning using the Python programming languages and Pythonbased machine learning libraries, this book introduces the mathematical concepts behind machine learning algorithms, which is essential for using machine learning successfully. Thus, this book is different from a purely practical book; it is a book that discusses the necessary details regarding machine learning concepts and offers intuitive yet informative explanations of how machine learning algorithms work, how to use them, and most importantly, how to avoid the most common pitfalls. Currently, if you type "machine learning" as a search term in Google Scholar, it returns an overwhelmingly large number of publications—1,800,000. Of course, we cannot discuss the nittygritty of all the different algorithms and applications that have emerged in the last 60 years. However, in this book, we will embark on an exciting journey that covers all the essential topics and concepts to give you a head start in this field. If you find that your thirst for knowledge is not satisfied, this book references many useful resources that can be used to follow up on the essential breakthroughs in this field. If you have already studied machine learning theory in detail, this book will show you how to put your knowledge into practice. If you have used machine learning techniques before and want to gain more insight into how machine learning actually works, this book is for you. Don't worry if you are completely new to the machine learning field; you have even more reason to be excited. Here is a promise that machine learning will change the way you think about the problems you want to solve and will show you how to tackle them by unlocking the power of data. Before we dive deeper into the machine learning field, let's answer your most important question, "Why Python?" The answer is simple: it is powerful yet very accessible. Python has become the most popular programming language for data science because it allows us to forget about the tedious parts of programming and offers us an environment where we can quickly jot down our ideas and put concepts directly into action. We, the authors, can truly say that the study of machine learning has made us better scientists, thinkers, and problem solvers. In this book, we want to share this knowledge with you. Knowledge is gained by learning. The key is our enthusiasm, and the real mastery of skills can only be achieved by practice. The road ahead may be bumpy on occasions and some topics may be more challenging than others, but we hope that you will embrace this opportunity and focus on the reward. Remember that we are on this journey together, and throughout this book, we will add many powerful techniques to your arsenal that will help us solve even the toughest problems the datadriven way.
 20.51MB
Python Machine Learning(2nd) 无水印英文高清完整.pdf版下载
20171007Python Machine Learning(2nd) 英文无水印pdf 第2版 pdf所有页面使用FoxitReader和PDFXChangeViewer测试都可以打开 本资源转载自网络，如有侵权，请联系上传者或csdn删除 本资源转载自网络，如有侵权，请联系上传者或csdn删除
 3.31MB
高级机器学习 Advanced Machine Learning with Python 英文高清.pdf版下载
20170503Table of Contents Chapter 1. Unsupervised Machine Learning Chapter 2. Deep Belief Networks Chapter 3. Stacked Denoising Autoencoders Chapter 4. Convolutional Neural Networks Chapter 5. SemiSupervised Learning Chapter 6. Text Feature Engineering Chapter 7. Feature Engineering Part II Chapter 8. Ensemble Methods Chapter 9. Additional Python Machine Learning Tools 通过掌握Python中的尖端机器学习技术来解决具有挑战性的数据科学问题 关于这本书 解决复杂的机器学习问题，探索深入学习 学习使用Python代码来实现一系列机器学习算法和技术 一个实用的教程，通过严谨有效的方法解决现实世界的计算问题 这本书是谁 此标题适用于Python开发人员和分析师或数据科学家，他们希望通过访问数据科学中最强大的一些最新趋势来增加现有技能。如果您曾经考虑过建立自己的图像或文字标签解决方案，或者进入Kaggle比赛，这本书是为您而设的！ 以前的Python经验和机器学习的一些核心概念的基础将是有帮助的。 你会学到什么 通过获得对尖端深度学习算法的实际和理论认识与顶尖数据科学家的竞争 应用您的新发现的技能来解决实际问题，通过对每种技术和测试的清晰解释的代码 自动化大量复杂数据，克服耗时的实践挑战 使用强大的功能工程技术提高模型的准确性和现有的输入数据 一起使用多种学习技巧来提高结果的一致性 使用一系列无监督技术了解数据集的隐藏结构 深入了解专家如何以有效，迭代和验证为重点的方法解决具有挑战性的数据问题 通过使用强大的组合技术将多个模型绑在一起，进一步提高您的深入学习模式的有效性 详细 这本书旨在为您带来最前沿的数据科学家今天使用的最相关和功能强大的机器学习技术的导游，这本书正是您将Python算法推向最大潜力所需要的。清晰的示例和详细的代码示例展示了深度学习技术，半监督学习和更多 – 同时使用包括图像，音乐，文本和财务数据在内的现实应用程序。 本书涵盖的机器学习技术处于商业实践的前沿。它们首次适用于图像识别，NLP和网络搜索，计算创意和商业/金融数据建模等领域。深度学习算法和模型集合正在由高科技和数字公司的数据科学家使用，但是在高需求的情况下成功应用所需的技能仍然很少。 本书旨在让读者参与最相关和强大的机器学习技术的导览。清楚描述技术的工作原理和详细的代码示例，在现实世界的应用中展示了深度学习技术，半监督学习等。我们还将了解NumPy和Theano。 在本书的这一端，您将学习一套先进的机器学习技术，并在特征选择和特征工程领域获得广泛的强大技能。 风格和方法 本书着重阐述复杂算法背后的理论和代码，使之具有实用性，可用性和理解力。每个主题都用现实世界的应用程序描述，提供广泛的上下文覆盖和详细的指导。 目录 第1章无监督机器学习 第二章深信仰网络 第3章堆叠去噪自动编码器 第四章卷积神经网络 第五章半监督学习 第六章文本特征工程 第七章特征工程第二部分 第八章合奏方法 其他Python机器学习工具
 656KB
Deep Learning in Python: Master Data Science and Machine Learning with Modern
20180730Deep learning is making waves. At the time of this writing (March 2016), Google’s AlghaGo program just beat 9dan professional Go player Lee Sedol at the game of Go, a Chinese board game. Experts in the field of Artificial Intelligence thought we were 10 years away from achieving a victory against a top professional Go player, but progress seems to have accelerated! While deep learning is a complex subject, it is not any more difficult to learn than any other machine learning algorithm. I wrote this book to introduce you to the basics of neural networks. You will get along fine with undergraduatelevel math and programming skill. All the materials in this book can be downloaded and installed for free. We will use the Python programming language, along with the numerical computing library Numpy. I will also show you in the later chapters how to build a deep network using Theano and TensorFlow, which are libraries built specifically for deep learning and can accelerate computation by taking advantage of the GPU. Unlike other machine learning algorithms, deep learning is particularly powerful because it automatically learns features. That means you don’t need to spend your time trying to come up with and test “kernels” or “interaction effects”  something only statisticians love to do. Instead, we will let the neural network learn these things for us. Each layer of the neural network learns a different abstraction than the previous layers. For example, in image classification, the first layer might learn different strokes, and in the next layer put the strokes together to learn shapes, and in the next layer put the shapes together to form facial features, and in the next layer have a high level representation of faces. On top of all this, deep learning is known for winning its fair share Kaggle contests. These are machine learning contests that are open to anyone in the world who are allowed to use any machine learning technique they want. Deep learning is that powerful. Do you want a gentle introduction to this “dark art”, with practical code examples that you can try right away and apply to your own data? Then this book is for you. Who is this book NOT for? Deep Learning and Neural Networks are usually taught at the upperyear undergraduate level. That should give you some idea of the type of knowledge you need to understand this kind of material. You absolutely need exposure to calculus to understand deep learning, no matter how simple the instructor makes things. Linear algebra would help. I will assume familiarity with Python (although it is an easy language to pick up). You will need to have some concept of machine learning. If you know about algorithms like logistic regression already, this book is perfect for you. If not, you might want to check out my “prerequisites” book, at: http://amzn.com/B01D7GDRQ2 On the other hand, this book is more like a casual primer than a dry textbook. If you are looking for material on more advanced topics, like LSTMs, convolutional neural networks, or reinforcement learning, I have online courses that teach this material, for example: https://www.udemy.com/deeplearningconvolutionalneuralnetworkstheanotensorflow New libraries like TensorFlow are being updated constantly. This is not an encyclopedia for these libraries (as such a thing would be impossible to keep up to date). In the one (1!!!) month since the book was first published, no less than THREE new wrapper libraries for TensorFlow have been released to make coding deep networks easier. To try and incorporate every little update would not only be impossible, but would continually cause parts of the book to be obsolete. Nobody wants that. This book, rather, includes fundamentals. Understanding these building blocks will make tackling these new libraries and features a piece of cake  that is my goal.
 1.3MB
Deep Learning With Python （中文尝鲜版）
20180902深度学习：Python 教程 (Deep Learning With Python) Deep Learning With Python: Develop Deep Learning Models on Theano and TensorFlow Using Keras 使用 Keras、Python、Theano 和 TensorFlow 开发深度学习模型 原书网站： https://machinelearningmastery.com/deeplearningwithpython/ 作者：Jason Brownlee 
 deep learning with Python（弗朗索瓦·肖莱） 笔记（一） 14120200722第一部分： 深度学习基础 第一章：什么是深度学习？ 本章介绍了基本的AI和机器学习以及深度学习的区别和关系，以及他们的大概发展，还有未来趋势。 主要点： 本书最主要想表达的思想就是：我们需要从能够从噪声中识别出信号，从而在过度炒作的新闻稿中发现改变世界的重大进展。 1.AI，Machine learning and deep learning的概念和区别： AI：让机器像人一样能够自动的智能的解决问题，完成任务。 机器学习：在预先定义好的可能性空间中，利用反馈信号的指引来寻找输入数...

下载
智慧旅游游客互动信息管理智能识别整体解决方案（66页2020）.ppt
智慧旅游游客互动信息管理智能识别整体解决方案（66页2020）.ppt

下载
中国移动DICT智慧园区解决方案（修订V2.0）.pptx
中国移动DICT智慧园区解决方案（修订V2.0）.pptx

下载
20210518中信证券信视角看债：政策纷至沓来，城投何去何从.pdf
20210518中信证券信视角看债：政策纷至沓来，城投何去何从.pdf

下载
北京电信助力园区行业疫情防控解决方案2020年02.pptx
北京电信助力园区行业疫情防控解决方案2020年02.pptx

下载
恩施联通“智慧旅游”物联网云计算整体方案汇报.ppt
恩施联通“智慧旅游”物联网云计算整体方案汇报.ppt

下载
大华企业生产园区综合解决方案v3.180页5.docx
大华企业生产园区综合解决方案v3.180页5.docx

下载
中国电信智慧旅游解决方案（58页2020）.ppt
中国电信智慧旅游解决方案（58页2020）.ppt

下载
大华楼宇园区人脸考勤.docx
大华楼宇园区人脸考勤.docx

下载
Mybatis.docx
Mybatis.docx

下载
20210518中金公司传媒互联网行业数据月报：4月中国手游市场同比持续增长.pdf
20210518中金公司传媒互联网行业数据月报：4月中国手游市场同比持续增长.pdf