The artificial intellegent (AI) is a popular searching keyword which could be heard in any field, e.g. marketing, robotics, art, biologics, physics anaylsis etc.. The AI is expected to help the human to sovle the complicated problem. It can be categorized to the strong AI and weak AI. The strong AI is defined the agent can think and act as a human, i.e. it have mind and mental state. But the weak AI can think and act rationally, i.e. it act intellegently. The weak AI is much common for applications in nowadays. It can be achived by several ways to have good perfomance for sovling the problem by buliding the analytic algorithm, rules, learning models, logic planing etc...
However, the learning models, so called machine learning (ML), is specially being an important and well-known branch in the AI. It is widely useful in writting recognition, image recognition, parameters optimation etc.. On the other hand, its extended and more complicated alogrithm is also revived and growing being a new trend of learning algorithm, it is called Nueral Network (or Deeplearning, if the network is deep). Thus the workboos written here are focusing on the basic models in machine learning, and giving the claer fundamental structures of nueral network. Since the technologies is improving rapidly, and having the insight and spirit of the machine learning and nueral netwrok is very important, the fundamental theories and applitcations of them have to be understood clearly. Credicted to several great experts arround the corners of the world, we are lucky to have several tools and books to learn and appproch the knowledges elegantly.
The two workbooks projects here are based on the books, Python Machine Learning, Sebastian Raschka and Neural Network And Deeplearning, Michael Nielsen, which demonstrate the Machine learning, Nueral Network and Deeplearning. The contains are also refered to many good materials, details see the Reference resource.
All the technologies and theories which inspire and lead the following two workbooks are from several online metirials, courses and books, which I have learned since June of 2017. These good resources of AI and machine learning knowledges are well collected, recored and listed in 👉 📁 resources.md. For a beginner, they can help you to know the insight of machine learning systematically, rather than only learning the coding techniques or the tool usages. I beilive "technology comes, technology go, but insight is forever".
⚠️ If the example code (*.ipynb) can't be loaded, please "copy" its Github URL and "paste" to nbviewer ⚠️
The programing language focus on Python, and the packages of machine learning models are using scikit-learn, pandas and numpy. The performance and visualization for analysis are using matplotlib and jupyter notebook in ipython. They demonstrates the processes of analysis from data. Sometimes Scipy are used.
All the main contants and knowledges are refered to the book Python Machine Learning, Sebastian Raschka. Several detial theories and mathematical methods are inspired by the book Pattern Recognition and Machine Learning, Christopher M. Bishop , and online courses, detail listed in resources.md.
（Pictures credited by link-1 and 2)
The programing language focus on Python and numpy. The performance and visualization for analysis are using matplotlib and jupyter notebook in ipython. They demonstrates the process of machine learning from data. The main contants and knowledges are refered to the book Neural Network And Deeplearning, Michael Nielsen and online courses, detail listed in resources.md.
（Picture credited by link)