Etaiva's Stars
mattermost/mattermost-handbook
Mattermost Community and Staff Handbook
0xk1h0/ChatGPT_DAN
ChatGPT DAN, Jailbreaks prompt
PlexPt/awesome-chatgpt-prompts-zh
ChatGPT 中文调教指南。各种场景使用指南。学习怎么让它听你的话。
appwrite/appwrite
Your backend, minus the hassle.
aqlaboratory/openfold
Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2
dia2018/What-is-the-Difference-Between-AI-and-Machine-Learning
Artificial Intelligence and Machine Learning have empowered our lives to a large extent. The number of advancements made in this space has revolutionized our society and continue making society a better place to live in. In terms of perception, both Artificial Intelligence and Machine Learning are often used in the same context which leads to confusion. AI is the concept in which machine makes smart decisions whereas Machine Learning is a sub-field of AI which makes decisions while learning patterns from the input data. In this blog, we would dissect each term and understand how Artificial Intelligence and Machine Learning are related to each other. What is Artificial Intelligence? The term Artificial Intelligence was recognized first in the year 1956 by John Mccarthy in an AI conference. In layman terms, Artificial Intelligence is about creating intelligent machines which could perform human-like actions. AI is not a modern-day phenomenon. In fact, it has been around since the advent of computers. The only thing that has changed is how we perceive AI and define its applications in the present world. The exponential growth of AI in the last decade or so has affected every sphere of our lives. Starting from a simple google search which gives the best results of a query to the creation of Siri or Alexa, one of the significant breakthroughs of the 21st century is Artificial Intelligence. The Four types of Artificial Intelligence are:- Reactive AI – This type of AI lacks historical data to perform actions, and completely reacts to a certain action taken at the moment. It works on the principle of Deep Reinforcement learning where a prize is awarded for any successful action and penalized vice versa. Google’s AlphaGo defeated experts in Go using this approach. Limited Memory – In the case of the limited memory, the past data is kept on adding to the memory. For example, in the case of selecting the best restaurant, the past locations would be taken into account and would be suggested accordingly. Theory of Mind – Such type of AI is yet to be built as it involves dealing with human emotions, and psychology. Face and gesture detection comes close but nothing advanced enough to understand human emotions. Self-Aware – This is the future advancement of AI which could configure self-representations. The machines could be conscious, and super-intelligent. Two of the most common usage of AI is in the field of Computer Vision, and Natural Language Processing. Computer Vision is the study of identifying objects such as Face Recognition, Real-time object detection, and so on. Detection of such movements could go a long way in analyzing the sentiments conveyed by a human being. Natural Language Processing, on the other hand, deals with textual data to extract insights or sentiments from it. From ChatBot Development to Speech Recognition like Amazon’s Alexa or Apple’s Siri all uses Natural Language to extract relevant meaning from the data. It is one of the widely popular fields of AI which has found its usefulness in every organization. One other application of AI which has gained popularity in recent times is the self-driving cars. It uses reinforcement learning technique to learn its best moves and identify the restrictions or blockage in front of the road. Many automobile companies are gradually adopting the concept of self-driving cars. What is Machine Learning? Machine Learning is a state-of-the-art subset of Artificial Intelligence which let machines learn from past data, and make accurate predictions. Machine Learning has been around for decades, and the first ML application that got popular was the Email Spam Filter Classification. The system is trained with a set of emails labeled as ‘spam’ and ‘not spam’ known as the training instance. Then a new set of unknown emails is fed to the trained system which then categorizes it as ‘spam’ or ‘not spam.’ All these predictions are made by a certain group of Regression, and Classification algorithms like – Linear Regression, Logistic Regression, Decision Tree, Random Forest, XGBoost, and so on. The usability of these algorithms varies based on the problem statement and the data set in operation. Along with these basic algorithms, a sub-field of Machine Learning which has gained immense popularity in recent times is Deep Learning. However, Deep Learning requires enormous computational power and works best with a massive amount of data. It uses neural networks whose architecture is similar to the human brain. Machine Learning could be subdivided into three categories – Supervised Learning – In supervised learning problems, both the input feature and the corresponding target variable is present in the dataset. Unsupervised Learning – The dataset is not labeled in an unsupervised learning problem i.e., only the input features are present, but not the target variable. The algorithms need to find out the separate clusters in the dataset based on certain patterns. Reinforcement Learning – In this type of problems, the learner is rewarded with a prize for every correct move, and penalized for every incorrect move. The application of Machine Learning is diversified in various domains like Banking, Healthcare, Retail, etc. One of the use cases in the banking industry is predicting the probability of credit loan default by a borrower given its past transactions, credit history, debt ratio, annual income, and so on. In Healthcare, Machine Learning is often been used to predict patient’s stay in the hospital, the likelihood of occurrence of a disease, identifying abnormal patterns in the cell, etc. Many software companies have incorporated Machine Learning in their workflow to steadfast the process of testing. Various manual, repetitive tasks are being replaced by machine learning models. Comparison Between AI and Machine Learning Machine Learning is the subset of Artificial Intelligence which has taken the advancement in AI to a whole new level. The thought behind letting the computer learn from themselves and voluminous data that are getting generated from various sources in the present world has led to the emergence of Machine Learning. In Machine Learning, the concept of neural networks plays a significant role in allowing the system to learn from themselves as well as maintaining its speed, and accuracy. The group of neural nets lets a model rectifying its prior decision and make a more accurate prediction next time. Artificial Intelligence is about acquiring knowledge and applying them to ensure success instead of accuracy. It makes the computer intelligent to make smart decisions on its own akin to the decisions made by a human being. The more complex the problem is, the better it is for AI to solve the complexity. On the other hand, Machine Learning is mostly about acquiring knowledge and maintaining better accuracy instead of success. The primary aim is to learn from the data to automate specific tasks. The possibilities around Machine Learning and Neural Networks are endless. A set of sentiments could be understood from raw text. A machine learning application could also listen to music, and even play a piece of appropriate music based on a person’s mood. NLP, a field of AI which has made some ground-breaking innovations in recent years uses Machine Learning to understand the nuances in natural language and learn to respond accordingly. Different sectors like banking, healthcare, manufacturing, etc., are reaping the benefits of Artificial Intelligence, particularly Machine Learning. Several tedious tasks are getting automated through ML which saves both time and money. Machine Learning has been sold these days consistently by marketers even before it has reached its full potential. AI could be seen as something of the old by the marketers who believe Machine Learning is the Holy Grail in the field of analytics. The future is not far when we would see human-like AI. The rapid advancement in technology has taken us closer than ever before to inevitability. The recent progress in the working AI is much down to how Machine Learning operates. Both Artificial Intelligence and Machine Learning has its own business applications and its usage is completely dependent on the requirements of an organization. AI is an age-old concept with Machine Learning picking up the pace in recent times. Companies like TCS, Infosys are yet to unleash the full potential of Machine Learning and trying to incorporate ML in their applications to keep pace with the rapidly growing Analytics space. Conclusion The hype around Artificial Intelligence and Machine Learning are such that various companies and even individuals want to master the skills without even knowing the difference between the two. Often both the terms are misused in the same context. To master Machine Learning, one needs to have a natural intuition about the data, ask the right questions, and find out the correct algorithms to use to build a model. It often doesn’t requiem how computational capacity. On the other hand, AI is about building intelligent systems which require advanced tools and techniques and often used in big companies like Google, Facebook, etc. There is a whole host of resources to master Machine Learning and AI. The Data Science blogs of Dimensionless is a good place to start with. Also, There are Online Data Science Courses which cover the various nitty gritty of Machine Learning.
PBSWE/Harvard
A cluster of exercise Labs from Harvard CS109 Class (2015) on Data Science (DS) and Machine Learning (ML) predictive models as I followed along and updated the standard to Python 3.x. The original class was forked from: https://github.com/cs109/2015/tree/master/Lectures Needless to say, I own nothing on the content and all rights go to the original creators. I simply studied this class and uploaded my progress as I went along, updating the Python standard from 2.x to 3.x where applicable. Cheers!
MikeBenitez1/Ximbal-App
Personal project which is an Android Java App composed by local signup/login with sqlite, a main home where you can go to a section to see on realtime the collected OBDII data (With an open source OBDII Java API), a section with driving advices and a section where you can register a car and maintenance agenda, it was developed to obtain a Master's degree on Information Technologies focused on Data Science.
nawaz-kmr/Real-time-Human-Detection-and-Counting
In this python project, we are going to build the Human Detection and Counting System through Webcam or you can give your own video or images. This is a deep learning project on computer vision, which will help you to master the concepts and make you an expert in the field of Data Science. Let’s build an exciting project.
newking9088/MITx-6.431x-Probability---The-Science-of-Uncertainty-and-Data
A guide on how to use the wealth of available material This class provides you with a great wealth of material, perhaps more than you can fully digest. This “guide" offers some tips about how to use this material. Start with the overview of a unit, when available. This will help you get an overview of what is to happen next. Similarly, at the end of a unit, watch the unit summary to consolidate your understanding of the “big picture" and of the relation between different concepts. Watch the lecture videos. You may want to download the slides (clean or annotated) at the beginning of each lecture, especially if you cannot receive high-quality streaming video. Some of the lecture clips proceed at a moderate speed. Whenever you feel comfortable, you may want to speed up the video and run it faster, at 1.5x. Do the exercises! The exercises that follow most of the lecture clips are a most critical part of this class. Some of the exercises are simple adaptations of you may have just heard. Other exercises will require more thought. Do your best to solve them right after each clip — do not defer this for later – so that you can consolidate your understanding. After your attempt, whether successful or not, do look at the solutions, which you will be able to see as soon as you submit your own answers. Solved problems and additional materials. In most of the units, we are providing you with many problems that are solved by members of our staff. We provide both video clips and written solutions. Depending on your learning style, you may pick and choose which format to focus on. But in either case, it is important that you get exposed to a large number of problems. The textbook. If you have access to the textbook, you can find more precise statements of what was discussed in lecture, additional facts, as well as several examples. While the textbook is recommended, the materials provided by this course are self-contained. See the “Textbook information" tab in Unit 0 for more details. Problem sets. One can really master the subject only by solving problems – a large number of them. Some of the problems will be straightforward applications of what you have learned. A few of them will be more challenging. Do not despair if you cannot solve a problem – no one is expected to do everything perfectly. However, once the problem set solutions are released (which will happen on the due date of the problem set), make sure to go over the solutions to those problems that you could not solve correctly. Exams. The midterm exams are designed so that in an on-campus version, learners would be given two hours. The final exam is designed so that in an on-campus version, learners would be given three hours. You should not expect to spend much more than this amount of time on them. In this respect, those weeks that have exams (and no problem sets!) will not have higher demands on your time. The level of difficulty of exam questions will be somewhere between the lecture exercises and homework problems. Time management. The corresponding on-campus class is designed so that students with appropriate prerequisites spend about 12 hours each week on lectures, recitations, readings, and homework. You should expect a comparable effort, or more if you need to catch up on background material. In a typical week, there will be 2 hours of lecture clips, but it might take you 4-5 hours when you add the time spent on exercises. Plan to spend another 3-4 hours watching solved problems and additional materials, and on textbook readings. Finally, expect about 4 hours spent on the weekly problem sets. Additional practice problems. For those of you who wish to dive even deeper into the subject, you can find a good collection of problems at the end of each chapter of the print edition of the book, whose solutions are available online.
elisavaliente/Machine-Learning-1
In this repository I am going to store some projects I built in my course of Machine Learning by Diego Bodas Sagi included in a master of Data Science for Finance.
korkridake/nypdshooting
Final Project 1 - NYPD Shooting Incident Data (DS as a Field, Master's Degree in Data Science of University of Colorado Boulder). A thorough analysis of every shooting incident that occurred in NYC going back to 2006 through the end of the previous calendar year.
ewalsh/masteringJavaDSplusScala
In this project I will go through the Mastering Java for Data Science Book while adding Scala versions for a joint project.
coralggarcia/TFM_MasterDataScience_KSchool
Here I'm going to publish my Master Project in Data Science
ArturoSanchezPalacio/Data_Science_Master
In this repository I am going to store all the work I did during my Master's Degree in Data Science for Finance.
Javier162380/R-exercises
In this repo I am going to upload some exercises performed in my data science master (http://www.masterdatascience.es/)
datasciencemasters/go
The Open Source Data Science Masters
florinpop17/app-ideas
A Collection of application ideas which can be used to improve your coding skills.
jwasham/coding-interview-university
A complete computer science study plan to become a software engineer.
EbookFoundation/free-programming-books
:books: Freely available programming books
kamranahmedse/design-patterns-for-humans
An ultra-simplified explanation to design patterns