Downloads & Free Reading Options - Results

This time on the show, pound include programming dot h! We're going to void main and Hello World all up in this biznitch. Python, JavaScript, BASIC? It's time to learn to code! All that and more this time on Hak5............return zero Source: https://www.youtube.com/watch?v=AmNZwW6833U Uploader: Hak5

We use and study a wide range of machine learning methods to predict and trade in the daily crypto currency market. We teach the algorithms to make daily market predictions based on how the 100 cryptocurrencies with the most market value change in price. Based on our research, all of the used models are able to make estimates that are statistically sound, with the average accuracy of all crypto currencies falling between 52.9% and 54.1%. When these accurate numbers are based on the 10% most confident expectations for each class and day, they go up to somewhere between 57.5% and 59.5%. A well-known case study in the field of data science looks at how people try to figure out how much different digital currencies are worth. Stock prices and the prices of cryptocurrencies are based on more than just the amount of buy and sell orders. At the moment, the government's financial policies about digital currencies affect how the prices of these things change. People's views about a crypto currency or a star who directly or indirectly backs a crypto currency can also cause a big rise in buying and selling of that currency. This study looks at the trustworthiness of the three most famous coins on the market today: bitcoin, how well buying strategies for ethereum and litecoin that are based on machine learning work. The models are checked and tested with both good and bad market situations. This lets us figure out how accurate the forecasts are in light of any changes in how the market feels between the proof and test times.

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (30 - Eclat)

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (34 - Part 7 Natural Language Processing)

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (38 - Part 9 Dimensionality Reduction)

Python competency is one of the most in-demand skills in the technical realm. Being a mainstay language, Python support many futuristic technologies such as Data Science, Machine Learning, Cloud Computing, Artificial Intelligence, and Data Visualization, learning Python has become indispensable. If you also want to dip your toes in Python programming , this blog has the right information for you. If you are in search of the Best Python Bootcamps in US, SynergisticIT is the first choice you can make.

The process of anticipating the stock market is one that is both difficult and time-consuming. On the other hand, advancements in stock market projection have begun to incorporate these methods of evaluating stock market data since the introduction of Machine Learning and its various algorithms. This has occurred since the beginning of the 21st century. We found that the LongShort Term Memory (LSTM) technique was the most effective when predicting stock values by using historical data. This was determined by analyzing the performance of the various algorithms in this endeavor. Because the algorithm has been taught using a massive accumulation of historical data and has been selected after being tested on a sample of data, it is going to be an excellent instrument for dealers and purchasers to utilize when they are investing in the stock market. According to the findings of this research, the machine learning model is superior to other machine learning models in terms of its ability to effectively predict market price.

Learning Python . 4th Ed. Mark Lutz. 2009. O'Reilly Part 1: getting started Part 2: types and operations Part 3: statements and syntax Part 4: functions Part 5: modules Part 6: classes and OOP Part 7: exceptions and tools Part 8: advanced topics sha256: 585922f4ad0178084035914fd20df932a0662c6de97c468e248201cb46f53874

Summary: I discuss how I use Python and some of the cool modules and libraries that I've found Source: http://hackerpublicradio.org/eps.php?id=1824 I'm Learning Some Python Lately I'm finally getting around to learning some Python. I wouldn't go as far as to say I'm learning it properly—that's not really my way—I'm kind of poking around in the dark learning things on an "as-needed" basis, but I'm finding that it's incredibly powerful and making me much more efficient in my daily life. In this podcast I discuss some of my favorite ways of using it and some of the cool modules and libraries that I've found that make things surprisingly easy in Python that used to be difficult for me in bash . What I Use It For Website build scripts, both for the School of Music and for my personal website. Converted from bash, tested and working fine on Windows and Mac. Text manipulation scripts, used in conjuction with blather. These do things like change text case, remove spaces, and so forth. Text entry. Voice commands insert various kinds of text templates or canned email responses for my classes. Also used in conjunction with blather. Adding or stripping HTML tags to/from selected text. Getting current weather conditions and forecasts, having results spoken back to me using system text-to-speech engine. Fun blather commands where I interact with my computer and have it talk back to me. Favorite Python Modules/Libraries pyperclip A cross-platform clipboard module for Python. (only handles plain text for now) https://pypi.python.org/pypi/pyperclip/1.5.11 pyttsx A Python package supporting common text-to-speech engines on Mac OS X, Windows, and Linux. https://pypi.python.org/pypi/pyttsx bs4 HTML parsing library. Beautiful Soup Documentation htmlmin A configurable HTML Minifier with safety features. https://pypi.python.org/pypi/htmlmin/ smartypants smartypants is a Python fork of SmartyPants , which easily translates "plain" ASCII punctuation characters into “smart” typographic punctuation HTML entities. titlecase Changes all words to Title Caps, and attempts to be clever about SMALL words like a/an/the in the input. https://pypi.python.org/pypi/titlecase swnamer A name generator that uses Star Wars characters, species and planets to create un fisique names. https://pypi.python.org/pypi/swnamer/0.1.0 Demo Screencasts Blather + Python: Insert Text from Predefined Nested Lists Using Voice Commands: https://www.youtube.com/watch?v=6futHS4JLsU Blather: "too much coffee?" python script: https://www.youtube.com/watch?v=GTDMi1zF76c

Deep Learning de A a la  Z - Redes Neuronales en Python desde Cero (2020)(Udemy)

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (15 - Part 3 Classification)

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (07 - Multiple Linear Regression)

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (20 - Naive Bayes)

Python for Computer Vision By Jose Poritla

hacker guide

Python is the lingua franca for data analytics and machine learning. Its superior productivity makes it the preferred tool for prototyping. However, traditional Python packages are not necessarily designed to provide high performance and scalability for large datasets. From this talk you will learn how to get close-to-native performance with Intel-optimized packages, such as numpy, scipy, and scikit-learn. The next part of the talk is focused on getting high performance and scalability from multi-cores on a single machine to large clusters of workstations. It will be demonstrated that with Python it is possible to achieve the same performance and scalability as with hand-tuned C++/MPI code: - Scalable Dataframe Compiler (SDC) makes possible to efficiently load and process huge datasets using pandas/Python. - A convenient Python API to data analytics and machine learning primitives (daal4py). While its interface is scikit-learn-like, its MPI-based engine allows to scale machine learning algorithms to bare-metal cluster performance. - From the talk you will learn how to use SDC and daal4py together to build an end-to-end analytics pipeline that scales to clusters, requiring only minimal code changes. Please see our speaker release agreement for details: https://ep2020.europython.eu/events/speaker-release-agreement/

By: Omar Sanseviero Event: PyConZA 2021 Url: https://2021.za.pycon.org/talks/37-building-machine-learning-demos-with-python/ About: How can you show what a Machine Learning model does once it's trained? In this talk, you're going to learn how to create Machine Learning apps and demos using Streamlit and Gradio, Python libraries for this purpose. Additionally, you'll see how to share them with the rest of the Open Source ecosystem. Learning to create graphic interfaces for models is extremely useful for sharing with other people interesting with them. Room: Video Room 2 Scheduled start: 2021-10-07 13:45:00 Sponsors: Gold: SPAN Digital: https://spandigital.com/ Takealot: http://takealot.com/ Andela: https://www.andela.com/ Silver: Python Software Foundation: https://www.python.org/psf/membership OfferZen: https://www.offerzen.com/ Patron: Thinkst Canary: https://canary.tools/ Afrolabs: http://www.afrolabs.co.za/

By: Omar Sanseviero Event: PyConZA 2021 Url: https://2021.za.pycon.org/talks/37-building-machine-learning-demos-with-python/ About: How can you show what a Machine Learning model does once it's trained? In this talk, you're going to learn how to create Machine Learning apps and demos using Streamlit and Gradio, Python libraries for this purpose. Additionally, you'll see how to share them with the rest of the Open Source ecosystem. Learning to create graphic interfaces for models is extremely useful for sharing with other people interesting with them. Room: Video Room 2 Scheduled start: 2021-10-07 13:45:00 Sponsors: Gold: SPAN Digital: https://spandigital.com/ Takealot: http://takealot.com/ Andela: https://www.andela.com/ Silver: Python Software Foundation: https://www.python.org/psf/membership OfferZen: https://www.offerzen.com/ Patron: Thinkst Canary: https://canary.tools/ Afrolabs: http://www.afrolabs.co.za/

This article is from Frontiers in Neuroinformatics , volume 8 . Abstract The aim in high-resolution connectomics is to reconstruct complete neuronal connectivity in a tissue. Currently, the only technology capable of resolving the smallest neuronal processes is electron microscopy (EM). Thus, a common approach to network reconstruction is to perform (error-prone) automatic segmentation of EM images, followed by manual proofreading by experts to fix errors. We have developed an algorithm and software library to not only improve the accuracy of the initial automatic segmentation, but also point out the image coordinates where it is likely to have made errors. Our software, called gala (graph-based active learning of agglomeration), improves the state of the art in agglomerative image segmentation. It is implemented in Python and makes extensive use of the scientific Python stack (numpy, scipy, networkx, scikit-learn, scikit-image, and others). We present here the software architecture of the gala library, and discuss several designs that we consider would be generally useful for other segmentation packages. We also discuss the current limitations of the gala library and how we intend to address them.

.

.

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (13 - Regression Model Selection in Python)

These are solar wind in situ data arrays in python pickle format suitable for machine learning, i.e. the arrays consist only of numbers, no strings and no datetime objects. See AAREADME_insitu_ML.txt for more explanation. If you use these data for peer reviewed scientific publications, please get in touch concerning usage and possible co-authorship by the authors (C. Möstl, A. J. Weiss, R. L. Bailey, A. Isavnin): [email protected] or twitter @chrisoutofspace Made with https://github.com/cmoestl/heliocats Load in python with e.g. for Parker Solar Probe data: > import pickle > filepsp='psp_2018_2019_sceq_ndarray.p' > [psp,hpsp]=pickle.load(open(filepsp, "rb" ) ) plot time vs total field > import matplotlib.pyplot as plt > plt.plot(psp['time'],psp['bt']) Times psp[:,0 ] or psp['time'] are in matplotlib format. Variable 'hpsp' contains a header with the variable names and units for each column. Coordinate systems for magnetic field components are RTN (Ulysses), SCEQ (Parker Solar Probe, STEREO-A/B, VEX, MESSENGER), HEEQ (Wind) available parameters: bt = total magnetic field bxyz = magnetic field components vt = total proton speed vxyz = velocity components (only for PSP) np = proton density tp = proton temperature xyz = spacecraft position in HEEQ r, lat, lon = spherical coordinates of position in HEEQ

Supervised Machine Learning with Python. Classification: Support Vector Machines

Mastering Git Switch, Python Learning Sites, and Debugging Ruby on Rails" is a comprehensive guide for developers aiming to enhance their skills in essential areas of modern software development. This blog covers three critical topics: mastering the git switch command in Git for efficient branch management, exploring the best Python learning sites for programmers, and delving into advanced debugging techniques for Ruby on Rails applications. Know more - https://stackify.com/

Python Machine Learning Third Edition Machine Learning and Deep Learning with Python,  scikit-learn, and TensorFlow 2 Sebastian Raschka Vahid Mirjalili

Humans communicate with one another using body language (gestures), such as hand and head gestures, facial expressions, lip movements, and so forth, or through natural language channels like words and writing. Sign language comprehension is just as crucial as knowing normal language. The primary means of communication for those who are hard of hearing is sign language. Without a translation, speaking with other hearing people can be difficult for those with hearing impairments. Because of this, the social lives of deaf people would be greatly improved by the installation of a system that recognizes sign language. In order to recognize the features of the hand in pictures captured by a webcam, we have presented in this study a marker-free, visual American Sign Language recognition system that makes use of image processing, computer vision, and neural network techniques. This paper deals with full phrase gestures that are used regularly every day and methods used to converted them to text. A number of image processing techniques have been used to identify the hand shape from continuous pictures. The Haar Cascade Classifier is used to determine the interpretation of signs and their associated meaning.

Programmers may be hesitant to use declarative systems, because of the associated learning curve. In this paper, we present an API that integrates the IDP Knowledge Base system into the Python programming language. IDP is a state-of-the-art logical system, which uses SAT, SMT, Logic Programming and Answer Set Programming technology. Python is currently one of the most widely used (teaching) languages for programming. The first goal of our API is to allow a Python programmer to use the declarative power of IDP, without needing to learn any new syntax or semantics. The second goal is allow IDP to be added to/removed from an existing code base with minimal changes.

Programmers may be hesitant to use declarative systems, because of the associated learning curve. In this paper, we present an API that integrates the IDP Knowledge Base system into the Python programming language. IDP is a state-of-the-art logical system, which uses SAT, SMT, Logic Programming and Answer Set Programming technology. Python is currently one of the most widely used (teaching) languages for programming. The first goal of our API is to allow a Python programmer to use the declarative power of IDP, without needing to learn any new syntax or semantics. The second goal is allow IDP to be added to/removed from an existing code base with minimal changes.

In questo video parleremo di un tema molto importante: come strutturare correttamente un progetto Python o Machine Learning.Strutturare correttamente un progetto Python serve prima di tutto a dare un senso a quello che state facendo e a rendere il codice immediatamente comprensibile, soprattutto a distanza di tempo, per poi stessi e anche per chi dovrà lavorare al vostro stesso progetto e quindi al vostro codice.Infatti, se avete del codice abbastanza \"incasinato\" e non strutturato correttamente, nel caso qualcuno (collaboratore, amico, conoscente, ecc.) dovesse aiutarvi nella ricerca di bugs o nell'implementazione di nuove funzionalità, dovrà prima di tutto capire come funziona il vostro programma, quali sono i flussi del software e come esso e organizzato. Risulterà quindi un'enorme perdita di tempo. Invece di concentrarvi nella vera risoluzione di un bug, dovrete prima di tutto cercare di capire come funziona il codice.Un progetto ben strutturato, e quindi anche un codice ben organizzato, vi consentirà un agevole refactor e un'agevole implementazione di nuove funzionalità.

Ankit Bahuguna - Query Embeddings: Web Scale Search powered by Deep Learning and Python [EuroPython 2016] [18 July 2016 / 2016-07-18] [Bilbao, Euskadi, Spain] (https://ep2016.europython.eu//conference/talks/query-embeddings) Query Embeddings is an unsupervised deep learning based system, built using Python and open source libraries (Annoy, keyvi etc.) which recognizes similarity between queries and their vector representations, for a web scale search engine integrated within Cliqz browser [https://cliqz.com/en]. It improves recall for previously unseen queries and is one of the many key components of our search stack. The framework be utilized by other low latency systems involving vector representations. ----- A web search engine allows a user to type few words of query and it presents list of potential relevant results within fraction of a second. Traditionally, keywords in the user query were fuzzy-matched in realtime with the keywords within different pages of the index and they didn't really focus on understanding meaning of query. Recently, Deep Learning + NLP techniques try to _represent sentences or documents as fixed dimensional vectors in high dimensional space_. These special vectors inherit semantics of the document. Query embeddings is an unsupervised deep learning based system, built using Python, Word2Vec, Annoy and Keyvi (https://github.com/cliqz- oss/keyvi) which recognizes similarity between queries and their vectors for a web scale search engine within Cliqz browser. (https://cliqz.com/en) ![][1] The goal is to describe how query embeddings contribute to our existing python search stack at scale and latency issues prevailing in real time search system. Also is a preview of separate vector index for queries, utilized by retrieval system at runtime via ANNs to get closest queries to user query, which is one of the many key components of our search stack. ![][2] Prerequisites: Basic experience in NLP, ML, Deep Learning, Web search and Vector Algebra. Libraries: Annoy. [1]: https://sites.google.com/site/netankit/1.png [2]: https://sites.google.com/site/netankit/3.png

I am testing

I am testing

I am testing

How To Think Like A Computer Scientist Learning Python

Formation Python spéciale Machine Learning francais. Apprendre Python en 30 vidéos qui contiennent une formation sur Numpy, Pandas, Matplotlib, Scipy, Sklearn, Seaborn, H5py, et bien d'autres techniques. Python est le langage d'excellence pour le machine learning, le deep learning, et la data science. En regardant cette série, vous deviendrez un expert python pour ces domaines en particulier. Merci de vous abonner ! 0:00 : Bienvenue dans cette formation gratuite ! 1:05 : Qui-suis je ? 2:00 : Programme de cette formation de 30 vidéos 3:44 : Comment Installer Python sur votre Ordinateur (Anaconda) 5:11 : Tutoriel Spyder 6:00 : Tutoriel Jupyter Notebook 6:25 : Installer des packages dans Anaconda 7:39 : Invitez-vos amis a vous rejoindre dans cette formation :) ► Le site d'Anaconda a été mis a jour depuis ma video, voici le lien pour télécharger Anaconda : https://www.anaconda.com/products/individual ► Me soutenir financierement sur Tipeee ou Utip (et obtenir des vidéos BONUS) https://fr.tipeee.com/machine-learnia https://utip.io/machinelearnia/ ► REJOINS NOTRE COMMUNAUTÉ DISCORD https://discord.gg/WMvHpzu ► ARTICLE EN COMPLÉMENT DE CETTE VIDÉO: https://machinelearnia.com/ ► Recevez gratuitement mon Livre: APPRENDRE LE MACHINE LEARNING EN UNE SEMAINE CLIQUEZ ICI: https://machinelearnia.com/apprendre-le-machine-learning-en-une-semaine/ ► Téléchargez gratuitement mes codes sur github: https://github.com/MachineLearnia ► Abonnez-vous : https://www.youtube.com/channel/UCmpptkXu8iIFe6kfDK5o7VQ ► Pour En Savoir plus : Visitez Machine Learnia : https://machinelearnia.com/ ► Qui suis-je ? Je m’appelle Guillaume Saint-Cirgue et je suis Data Scientist au Royaume Uni. Après avoir suivi un parcours classique maths sup maths spé et avoir intégré une bonne école d’ingénieur, je me suis tourné vers l’intelligence artificielle de ma propre initiative et j’ai commencé à apprendre tout seul le machine learning et le deep learning en suivant des formations payantes, en lisant des articles scientifiques, en suivant les cours du MIT et de Stanford et en passant des week end entier à développer mes propres codes. Aujourd’hui, je veux vous offrir ce que j’ai appris gratuitement car le monde a urgemment besoin de se former en Intelligence Artificielle. Que vous souhaitiez changer de vie, de carrière, ou bien développer vos compétences à résoudre des problèmes, ma chaîne vous y aidera. C’est votre tour de passer à l’action ! ► Une question ? Contactez-moi: [email protected]

Includes index

Train passenger forecasting assists in planning, resource use, and system management. forecasts rail ridership. Train passenger predictions help prevent stranded passengers and empty seats. Simulating rail transport requires a low-error model. We developed a fuzzy time series forecasting model. Using historical data was the goal. This concept predicts future railway passengers using Holt's double exponential smoothing (DES) and a fuzzy time series technique based on a rate-of-change algorithm. Holt's DES predicts the next period using a fuzzy time series and the rate of change. This method improves prediction accuracy by using event discretization. positive, since changing dynamics reveal trends and seasonality. It uses event discretization and machine-learning-optimized frequency partitioning. The suggested method is compared to existing train passenger forecasting methods. This study has a low average forecasting error and a mean squared error.

.

289+ Machine Learning Projects With Python Code

Cover

Executing machine learning models in a production environment can be tricky, especially at a major bank where compliance and risk are carefully taken into account. In this talk I explain how, we, at ING (a large bank operating on global scale), execute our Python models in a production environment by building minimal Docker images for python versions. I will first talk about the possible security risks of running any docker container in a production environment. Then I will talk about ways in which we can make Docker containers more secure by building minimal docker images for Python. Finally I will explain how these docker images are used in practice to serve machine learning models at ING. Prerequisites: - Some basic knowledge of Docker can be helpful - Some basic understanding of security can be helpful Goals: - Understand the security risks of running docker containers - Know how to make docker images more secure - How to build secure model serving docker images Please see our speaker release agreement for details: https://ep2019.europython.eu/events/speaker-release-agreement/

Sixteen scholarly papers and twenty abstracts comprise the content of the eighteenth annual Brick & Click Libraries Conference, held annually at Northwest Missouri State University in Maryville, Missouri. The proceedings, authored by academic librarians and presented at the conference, portray the contemporary and future face of librarianship. The 2018 paper and abstract titles include: (1) Committee on Diversity & Inclusion: Cultivating an Inclusive Library Environment (Orolando Duffus, Andrea Malone, Margaret Dunn, Lisa Cruces, Matthew Moore, Annie Wu, and Frederick Young); (2) Checking Out the LGBT+ (Kayla Reed); (3) Tailoring Library Instruction to Adult Students: Applying the Science and Methods of Andragogy for Modern Instructional and Reference Services (Eric Deatherage and Jason Smith); (4) Library-Faculty Collaboration for OER Promotion and Implementation (Paula Martin); (5) The Facts of Fiction: Research for Creative Writers (Addison Lucchi); (6) Location and the Collection Connection (Kayla Reed and Amber Carr); (7) Gay for No Pay: How to Maintain an LGBTQ+ Collection with No Budget (Rachel Wexelbaum); (8) A Step Up: Piloting Integrated Information Literacy Instruction Throughout a Discipline (Nathan Elwood and Robyn Hartman); (9) Not Just a Collection: The Emergence and Evolution of Our Contemporary Collection (Hong Li and Kayla Reed); (10) Flipster: How One Community College Library Supports Faculty and Student Academic Needs with Flipster Digital Magazines (Stephen Ambra); (11) Three Ring Circus: A Model for Understanding and Teaching Students about Bias (Virginia Cairns); (12) Demystifying DH: How to Get Started with Digital Humanities (Sherri Brown and Forstot Burke); (13) Academic Libraries Embracing Technology with a Purpose (Lavoris Martin); (14) (A)ffective Management: A People First Management Approach (Ryan Weir); (15) Plugged & Unplugged Active Learning Strategies for One Shots (Judy Bastin, Justina Mollach, Leslie Pierson, Ruth Harries, and Teresa Mayginnes); (16) Giving a Booster Shot to Your One Shot: Incorporating Engaging Activities into Library Instruction (Kelly Leahy, Gwen Wilson, and Angela Beatie); (17) Adventures with Omeka.net: Metadata, Workflows, and Exhibit-based Storytelling at UNO Libraries (Yumi Ohira, Angela Kroeger, and Lori Schwartz); (18) Online Badge Classes for High School Students (Angela Paul); (19) Fake News: The Fun, the Fear, and the Future of Resource Evaluation (Lindsay Brownfield); (20) Making Outreach the Library's Mission (April K. Miller); (21) Active Learning for Metaliteracies: Digital Modules from the New Literacies Alliance (Rachel R. Vukas, Prasanna Vaduvathiriyan, and Brenda Linares); (22) Calculating Return on Investment in Libraries (Nicholas Wyant); (23) Crossing Borders: Expanding Digitization Efforts Across Library Departments (Jay Trask, Jane Monson, and Jessica Hayden); (24) From Silos to Collaboration (Joyce Meldrem); (25) Key Performance Indicator Tracking Using Google Forms (Joshua Lambert); (26) Bridging the Gap: Providing Equal Access of Library Resources and Services to Distance Learners (Nancy Crabtree, Xiaocan (Lucy) Wang, Bob Black); (27) Coming to the Plains: Latino/a Stories in Nebraska (Laurinda Weisse, Michelle Warren, and Jacob Rosdail); (28) Five Keys to #SocialMediaSuccess in Academic Libraries (Hannah E. Christian and Alison Hanner); (29) Easy Information Literacy Assessments for Small Academic Libraries (Julie Pinnell); (30) Traversing the Path: A Library Director's Guide to the Higher Learning Commission's Open Pathway for Accreditation (Sandy Moore); (31) Drawing Magic: Visualizing the Internet to Introduce Information Literacy (Kelly Leahy); (32) Chatspeak for Librarians: Best Practices for Chat Reference (Tanner D. Lewey); (33) The Creative Learning Spiral: A Python Learner in the Library (Greta Valentine); (34) The Poet's Papers: Literary Research in the Small College Archives (Martha A. Tanner); (35) Giving Students an Edge: Enhancing Resumes with a Digital Information Research Certificate (Rachel R. Vukas); and (36) Where Did You Get That eBook? Comparison of Student/Faculty Use of eBooks, Library Space, and Citation Management Programs (Alice B. Ruleman). (Individual papers contain references.) [For the 2017 proceedings, see ED578189.]

Sixteen scholarly papers and twenty abstracts comprise the content of the eighteenth annual Brick & Click Libraries Conference, held annually at Northwest Missouri State University in Maryville, Missouri. The proceedings, authored by academic librarians and presented at the conference, portray the contemporary and future face of librarianship. The 2018 paper and abstract titles include: (1) Committee on Diversity & Inclusion: Cultivating an Inclusive Library Environment (Orolando Duffus, Andrea Malone, Margaret Dunn, Lisa Cruces, Matthew Moore, Annie Wu, and Frederick Young); (2) Checking Out the LGBT+ (Kayla Reed); (3) Tailoring Library Instruction to Adult Students: Applying the Science and Methods of Andragogy for Modern Instructional and Reference Services (Eric Deatherage and Jason Smith); (4) Library-Faculty Collaboration for OER Promotion and Implementation (Paula Martin); (5) The Facts of Fiction: Research for Creative Writers (Addison Lucchi); (6) Location and the Collection Connection (Kayla Reed and Amber Carr); (7) Gay for No Pay: How to Maintain an LGBTQ+ Collection with No Budget (Rachel Wexelbaum); (8) A Step Up: Piloting Integrated Information Literacy Instruction Throughout a Discipline (Nathan Elwood and Robyn Hartman); (9) Not Just a Collection: The Emergence and Evolution of Our Contemporary Collection (Hong Li and Kayla Reed); (10) Flipster: How One Community College Library Supports Faculty and Student Academic Needs with Flipster Digital Magazines (Stephen Ambra); (11) Three Ring Circus: A Model for Understanding and Teaching Students about Bias (Virginia Cairns); (12) Demystifying DH: How to Get Started with Digital Humanities (Sherri Brown and Forstot Burke); (13) Academic Libraries Embracing Technology with a Purpose (Lavoris Martin); (14) (A)ffective Management: A People First Management Approach (Ryan Weir); (15) Plugged & Unplugged Active Learning Strategies for One Shots (Judy Bastin, Justina Mollach, Leslie Pierson, Ruth Harries, and Teresa Mayginnes); (16) Giving a Booster Shot to Your One Shot: Incorporating Engaging Activities into Library Instruction (Kelly Leahy, Gwen Wilson, and Angela Beatie); (17) Adventures with Omeka.net: Metadata, Workflows, and Exhibit-based Storytelling at UNO Libraries (Yumi Ohira, Angela Kroeger, and Lori Schwartz); (18) Online Badge Classes for High School Students (Angela Paul); (19) Fake News: The Fun, the Fear, and the Future of Resource Evaluation (Lindsay Brownfield); (20) Making Outreach the Library's Mission (April K. Miller); (21) Active Learning for Metaliteracies: Digital Modules from the New Literacies Alliance (Rachel R. Vukas, Prasanna Vaduvathiriyan, and Brenda Linares); (22) Calculating Return on Investment in Libraries (Nicholas Wyant); (23) Crossing Borders: Expanding Digitization Efforts Across Library Departments (Jay Trask, Jane Monson, and Jessica Hayden); (24) From Silos to Collaboration (Joyce Meldrem); (25) Key Performance Indicator Tracking Using Google Forms (Joshua Lambert); (26) Bridging the Gap: Providing Equal Access of Library Resources and Services to Distance Learners (Nancy Crabtree, Xiaocan (Lucy) Wang, Bob Black); (27) Coming to the Plains: Latino/a Stories in Nebraska (Laurinda Weisse, Michelle Warren, and Jacob Rosdail); (28) Five Keys to #SocialMediaSuccess in Academic Libraries (Hannah E. Christian and Alison Hanner); (29) Easy Information Literacy Assessments for Small Academic Libraries (Julie Pinnell); (30) Traversing the Path: A Library Director's Guide to the Higher Learning Commission's Open Pathway for Accreditation (Sandy Moore); (31) Drawing Magic: Visualizing the Internet to Introduce Information Literacy (Kelly Leahy); (32) Chatspeak for Librarians: Best Practices for Chat Reference (Tanner D. Lewey); (33) The Creative Learning Spiral: A Python Learner in the Library (Greta Valentine); (34) The Poet's Papers: Literary Research in the Small College Archives (Martha A. Tanner); (35) Giving Students an Edge: Enhancing Resumes with a Digital Information Research Certificate (Rachel R. Vukas); and (36) Where Did You Get That eBook? Comparison of Student/Faculty Use of eBooks, Library Space, and Citation Management Programs (Alice B. Ruleman). (Individual papers contain references.) [For the 2017 proceedings, see ED578189.]

Introduction à la programmation Python pour faire du Machine Learning. Python est un langage interprété, et c'est le meilleur pour faire du Machine Learning en 2019 : il contient le plus de fonctionnalité et de ressource pour exceller en Machine Learning et en Deep Learning. Google, Facebook, Amazon utilisent tous Python pour leurs initiatives d'intelligence artificielle. Dans cette vidéo, je vous montre les structures de contrôle de base qu'il faut maîtriser pour écrire des algorithmes de machine learning. ► Timecode de la vidéo : 00:00 : Début 0:26 : Introduction 1:58 : Les Bases de Python 2:57 : Commentaires, Variables, Print 5:04 : IF - ELIF - ELSE 7:42 : Astuce Bonus 8:10 : Jupyter Notebook 9:27 : FOR LOOP 10:40 : WHILE LOOP 12:10 : Importer des Modules 14:02 : Créer nos propres fonctions ► Lien pour télécharger Anaconda : https://www.anaconda.com ► ARTICLE EN COMPLÉMENT DE CETTE VIDÉO: https://machinelearnia.com/ ► Soutenez-moi sur Tipeee pour du contenu BONUS: https://fr.tipeee.com/machine-learnia ► REJOINS NOTRE COMMUNAUTÉ DISCORD https://discord.gg/WMvHpzu ► Recevez gratuitement mon Livre: APPRENDRE LE MACHINE LEARNING EN UNE SEMAINE CLIQUEZ ICI: https://machinelearnia.com/apprendre-le-machine-learning-en-une-semaine/ ► Pour En Savoir plus : Visitez Machine Learnia : https://machinelearnia.com/ ► Qui suis-je ? Je m’appelle Guillaume Saint-Cirgue et je suis Data Scientist au Royaume Uni. Après avoir suivi un parcours classique maths sup maths spé et avoir intégré une bonne école d’ingénieur, je me suis tourné vers l’intelligence artificielle de ma propre initiative et j’ai commencé à apprendre tout seul le machine learning et le deep learning en suivant des formations payantes, en lisant des articles scientifiques, en suivant les cours du MIT et de Stanford et en passant des week end entier à développer mes propres codes. Aujourd’hui, je veux vous offrir ce que j’ai appris gratuitement car le monde a urgemment besoin de se former en Intelligence Artificielle. Que vous souhaitiez changer de vie, de carrière, ou bien développer vos compétences à résoudre des problèmes, ma chaîne vous y aidera. C’est votre tour de passer à l’action ! ► Une question ? Contactez-moi: [email protected]

Python for Brain Mining: (Neuro)science with State of the Art Machine Learning and Data Visualization Gael Varoquaux If you have questions, email [email protected]

Power of Machine Learning with python

Python-Introduction to Data Science and Machine learning

Python-Introduction to Data Science and Machine learning