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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

In this paper, a framework for testing Deep Neural Network (DNN) design in Python is presented. First, big data, machine learning (ML), and Artificial Neural Networks (ANNs) are discussed to familiarize the reader with the importance of such a system. Next, the benefits and detriments of implementing such a system in Python are presented. Lastly, the specifics of the system are explained, and some experimental results are presented to prove the effectiveness of the system.

Tobias Brandt https://2016.za.pycon.org/talks/16/ This talk will give an introduction to Neural Networks and Deep Learning in Python. We will cover some of the history of Neural Networks and obstacles that were encountered in the 1990s. This will then lead onto the developments in 2006 and 2012 that lead to the resurgence of interest in Neural Networks and the rebranding of the field as Deep Learning. These developments will be illustrated by means of an extended example of building a classifier of hand written digits on the MNIST dataset. We will start with a simple Multi-Layer Perceptron and then build this up into a Stacked Denoising Autoencoder. All code will be developed using the Keras framework and TensorFlow and can be run on a simple laptop.

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Python Machine Learning The Ultimate Beginners Guide to Learn Python Machine Learning Step by Step using Scikit-Learn by Reed, Mark

Learn how to Code:

Hello Machine

As the scale and scope of data collection continue to increase across virtually all fields, statistical learning has become a critical toolkit for anyone who wishes to understand data. An Introduction to Statistical Learning provides a broad and less technical treatment of key topics in statistical learning. This book is appropriate for anyone who wishes to use contemporary tools for data analysis.

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Nathan Brouwer https://www.pyohio.org/2024/program/talks/23-python-and-me-using-machine-learning-in-python-to-analyze We are over 20 years into the genomics era, with new insights into human health and our recent evolutionary history emerging almost daily. Genomics may seem like the province of PhDs and R\&D departments, but anyone with basic Python skills can navigate a genomics data pipeline and explore human genetic diversity, including their own! In this presentation, I will introduce the fields of population and health genomics, and the types of domain\-specific data used for genomics study. I will then demonstrate how you can use Python to visualize and analyze public data sources like the 1000 Genomes Project using unsupervised machine learning methods, and how to investigate your own genomics data from sources like *23andMe*. Since many people are not comfortable giving for\-profit companies access to their genomic data, I'll also show how you can simulate realistic personal genomic data via a supervised ML model. #PyOhio #Python The 17th annual PyOhio held July 27-28 in Cleveland, OH. === https://PyOhio.org Founded in 2008, PyOhio is a free annual Python programming language community conference based in Ohio. Content ranges from beginner to advanced and is intended to be relevant to all types of Python users: students, software professionals, scientists, hobbyists, and anyone looking to learn more. Sun Jul 28 14:45:00 2024 at Calypso Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1

Nathan Brouwer https://www.pyohio.org/2024/program/talks/23-python-and-me-using-machine-learning-in-python-to-analyze We are over 20 years into the genomics era, with new insights into human health and our recent evolutionary history emerging almost daily. Genomics may seem like the province of PhDs and R\&D departments, but anyone with basic Python skills can navigate a genomics data pipeline and explore human genetic diversity, including their own! In this presentation, I will introduce the fields of population and health genomics, and the types of domain\-specific data used for genomics study. I will then demonstrate how you can use Python to visualize and analyze public data sources like the 1000 Genomes Project using unsupervised machine learning methods, and how to investigate your own genomics data from sources like *23andMe*. Since many people are not comfortable giving for\-profit companies access to their genomic data, I'll also show how you can simulate realistic personal genomic data via a supervised ML model. #PyOhio #Python The 17th annual PyOhio held July 27-28 in Cleveland, OH. === https://PyOhio.org Founded in 2008, PyOhio is a free annual Python programming language community conference based in Ohio. Content ranges from beginner to advanced and is intended to be relevant to all types of Python users: students, software professionals, scientists, hobbyists, and anyone looking to learn more. Sun Jul 28 14:45:00 2024 at Calypso Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1

Shrey Somaiya https://2020.pycon.org.au/program/73HFNJ This talk is simply one about my experience of learning C from a python background - what python features helped me understand C better (control flow, algorithm and problem breakdown design, functions, etc) - what C features helped me understand python better (ie pointers in C helped me understand python lists, etc) - how I use my python knowledge to teach C at University python, pycon, australia, programming, conference, technical, pyconline, developers, panel, sessions, libraries, frameworks, community, sysadmins, students, education, data, science PyCon AU is the national conference for the Python programming community, bringing together professional, student and enthusiast developers, sysadmins and operations folk, students, educators, scientists, statisticians, and many others besides, all with a love for working with Python. PyCon AU informs the country's Python developers with presentations, tutorials and panel sessions by experts and core developers of Python, as well as the libraries and frameworks that they rely on. PyCon AU is the national conference for the Python programming community, bringing together professional, student and enthusiast developers, sysadmins and operations folk, students, educators, scientists, statisticians, and many others besides, all with a love for working with Python. PyCon AU informs the country's Python developers with presentations, tutorials and panel sessions by experts and core developers of Python, as well as the libraries and frameworks that they rely on. Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1 Python, PyCon, PyConAU, PyConline Fri Sep 4 10:25:00 2020 at Floperator

How To Think Like A Computer Scientist Learning Python

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

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Bayesian Machine Learning In Python AB Testing

Deep Learning Prerequisites Linear Regression in Python

Tensor methods are gaining increasing traction in machine learning. However, there are scant to no resources available to perform tensor learning and decomposition in Python. To answer this need we developed TensorLy. TensorLy is a state of the art general purpose library for tensor learning. Written in Python, it aims at following the same standards adopted by the main projects of the Python scientific community and fully integrating with these. It allows for fast and straightforward tensor decomposition and learning and comes with exhaustive tests, thorough documentation and minimal dependencies. It can be easily extended and its BSD licence makes it suitable for both academic and commercial applications. TensorLy is available at https://github.com/tensorly/tensorly

Tensor methods are gaining increasing traction in machine learning. However, there are scant to no resources available to perform tensor learning and decomposition in Python. To answer this need we developed TensorLy. TensorLy is a state of the art general purpose library for tensor learning. Written in Python, it aims at following the same standards adopted by the main projects of the Python scientific community and fully integrating with these. It allows for fast and straightforward tensor decomposition and learning and comes with exhaustive tests, thorough documentation and minimal dependencies. It can be easily extended and its BSD licence makes it suitable for both academic and commercial applications. TensorLy is available at https://github.com/tensorly/tensorly

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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/

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

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

mlpy is a Python Open Source Machine Learning library built on top of NumPy/SciPy and the GNU Scientific Libraries. mlpy provides a wide range of state-of-the-art machine learning methods for supervised and unsupervised problems and it is aimed at finding a reasonable compromise among modularity, maintainability, reproducibility, usability and efficiency. mlpy is multiplatform, it works with Python 2 and 3 and it is distributed under GPL3 at the website http://mlpy.fbk.eu.

Jupyter notebooks for the code samples of the book "Deep Learning with Python" Companion Jupyter notebooks for the book "Deep Learning with Python" This repository contains Jupyter notebooks implementing the code samples found in the book Deep Learning with Python (Manning Publications) . Note that the original text of the book features far more content than you will find in these notebooks, in particular further explanations and figures. Here we have only included the code samples themselves and immediately related surrounding comments. These notebooks use Python 3.6 and Keras 2.0.8. They were generated on a p2.xlarge EC2 instance. Table of contents Chapter 2: 2.1: A first look at a neural network Chapter 3: 3.5: Classifying movie reviews 3.6: Classifying newswires 3.7: Predicting house prices Chapter 4: 4.4: Underfitting and overfitting Chapter 5: 5.1: Introduction to convnets 5.2: Using convnets with small datasets 5.3: Using a pre-trained convnet 5.4: Visualizing what convnets learn Chapter 6: 6.1: One-hot encoding of words or characters 6.1: Using word embeddings 6.2: Understanding RNNs 6.3: Advanced usage of RNNs 6.4: Sequence processing with convnets Chapter 8: 8.1: Text generation with LSTM 8.2: Deep dream 8.3: Neural style transfer 8.4: Generating images with VAEs 8.5: Introduction to GANs To restore the repository, download the bundle fchollet-deep-learning-with-python-notebooks_-_2017-09-06_17-02-25.bundle and run: git clone fchollet-deep-learning-with-python-notebooks_-_2017-09-06_17-02-25.bundle -b master Source: https://github.com/fchollet/deep-learning-with-python-notebooks Uploader: fchollet Upload date: 2017-09-06

"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. Whether you're navigating version control, expanding your programming knowledge, or tackling complex Rails bugs, this guide offers valuable insights and practical tips to boost your productivity and expertise. Read more -  https://stackify.com/

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.

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.

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Cette Formation Python Numpy est un tutoriel français spécial machine learning: Numpy est le package python le plus important pour faire du machine learning et du data science. Numpy comprend le tableau array dit ndarray (n dimensions) qui est un objet extrêmement puissant en machine learning et data science. Numpy propose beaucoup de méthode pour le ndarray, dans cette vidéo nous voyons les différents constructeurs qui permettent d'initialiser les tableau ndarray: np.array() np.zeros() np.ones() np.full() np.random.randn() les deux attributs les plus importants à retenir sont : shape size pour développer des programmes puissants, pensez à définir le type de valeur dans le np.array() avec dtype = np.int16, np.float64 Nous voyons aussi les méthodes les plus utiles pour manipuler la forme de nos tableau numpy: np.vstack np.hstack np.concatenate np.reshape np.squeeze np.ravel Il n'y a rien de plus à retenir pour bien se lancer avec Numpy. Ignorez les autres attributs et méthodes pour le moment ! ► Timeline de la vidéo : 0:00 Intro 00:40 Le tableau Numpy, ses dimensions et sa shape 05:20 initialiser un ndarray: np.ones, np.zeros, 09:15 np.random.randn 12:04 np.linspace, np.arange 13:24 dtype=np.float16 np.float64 15:43 Assembler des tableaux: vstack hstack concatenate 18:40 np.reshape np.squeeze 22:10 np.ravel() 23:08 Exercice ► Soutenez-moi sur Tipeee pour du contenu BONUS: https://fr.tipeee.com/machine-learnia ► Documentation Numpy pour ndarray: https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html ► Documentation Numpy pour np.random: https://docs.scipy.org/doc/numpy-1.16.0/reference/routines.random.html ► ARTICLE EN COMPLÉMENT DE CETTE VIDÉO: https://machinelearnia.com/ ► 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/ ► Télécharger 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]

random3, 'Andrew Park - Data Science for Beginners_ 4 Books in 1_ Python Programming, Data Analysis, Machine Learning. A Complete Overview to Master The Art of Data Science From Scratch Using Python for Busines'

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.

Python has become the dominant programming language in the fields of Artificial Intelligence AI and Machine Learning ML due to its user friendly nature, versatility, and the vast array of libraries available to developers. This paper explores how Python facilitates the rapid development of AI and ML applications, particularly through popular frameworks such as TensorFlow, PyTorch, and Scikit learn. Additionally, a case study on image classification using Convolutional Neural Networks CNNs demonstrates Pythons practical applications in real world scenarios. The comparative analysis presented in this paper emphasizes Pythons effectiveness in fostering scalable and reproducible research in the field of AI. M. Yamuna "Python for Artificial Intelligence and Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-9 | Issue-3 , June 2025, URL: https://www.ijtsrd.com/papers/ijtsrd79847.pdf Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/79847/python-for-artificial-intelligence-and-machine-learning/m-yamuna

Python has become the dominant programming language in the fields of Artificial Intelligence AI and Machine Learning ML due to its user friendly nature, versatility, and the vast array of libraries available to developers. This paper explores how Python facilitates the rapid development of AI and ML applications, particularly through popular frameworks such as TensorFlow, PyTorch, and Scikit learn. Additionally, a case study on image classification using Convolutional Neural Networks CNNs demonstrates Pythons practical applications in real world scenarios. The comparative analysis presented in this paper emphasizes Pythons effectiveness in fostering scalable and reproducible research in the field of AI. M. Yamuna "Python for Artificial Intelligence and Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-9 | Issue-3 , June 2025, URL: https://www.ijtsrd.com/papers/ijtsrd79847.pdf Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/79847/python-for-artificial-intelligence-and-machine-learning/m-yamuna

Python has become the dominant programming language in the fields of Artificial Intelligence AI and Machine Learning ML due to its user friendly nature, versatility, and the vast array of libraries available to developers. This paper explores how Python facilitates the rapid development of AI and ML applications, particularly through popular frameworks such as TensorFlow, PyTorch, and Scikit learn. Additionally, a case study on image classification using Convolutional Neural Networks CNNs demonstrates Pythons practical applications in real world scenarios. The comparative analysis presented in this paper emphasizes Pythons effectiveness in fostering scalable and reproducible research in the field of AI. M. Yamuna "Python for Artificial Intelligence and Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-9 | Issue-3 , June 2025, URL: https://www.ijtsrd.com/papers/ijtsrd79847.pdf Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/79847/python-for-artificial-intelligence-and-machine-learning/m-yamuna

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]

Industrial Machine Learning This talk will provide key insights on the learnings I have obtained throughout my career building & deploying machine learning systems in critical environments across several sectors. I will provide a deep dive on how to build scalable and distributed machine learning data pipelines using Airflow with a Celery backend. I will also compare Airflow with other technologies available out there and how it differentiates, such as Luigi, Chronos, Pinball, etc. If you attend the talk, you will obtain an understanding on the solid fundamentals of Airflow, together with its caveats and walk-arounds for more complex use-cases. As we proceed with the examples, I will cover the challenges that you will run into when scaling Machine Learning systems, and how Airflow can be used to address these using a manager-worker-queue architecture for distributed processing with Celery. By the end of this talk you will have the knowledge required to build your own industry-ready machine learning pipelines to process data at scale, and I will provide further reading resources so people are able to implement the knowledge obtained almost right away. Please see our speaker release agreement for details: https://ep2018.europython.eu/en/speaker-release-agreement/

#มองโลกมองไทย ประจำวันที่ 25 ตุลาคม 2563 คุยกับคอมพิวเตอร์ด้วยภาษามนุษย์? ปราบ เลาหะโรจนพันธ์ อาจารย์พิเศษ วิชา Machine Learning ม.ธรรมศาสตร์ แขกรับเชิญพิเศษจะมาเล่าให้ฟังว่า 'ทักษะภาษาคอมพ์ฯ' ช่วยให้เราพลิกวิธีคิด พลิกอนาคตอย่างไร และทำไม โลกถึงนิยม และมองหาคนที่มีทักษะนี้เสียเหลือเกิน? Machine Learning สำหรับมนุษย์เงินเดือน โดย ThinkLab Creative Space & Cafe https://www.facebook.com/thinklab.creativespace/posts/194751758721175 พิเศษ! เพียงระบุว่ารับชมมาจากรายการ "มองโลก มองไทย" รับส่วนลด 10% Facebook Page: ThinkLab Creative Space and Cafe โทรศัพท์/ LINE ID: 0610247199 สมัครสมาชิกเพื่อรับสิทธิพิเศษ (Membership) https://www.youtube.com/channel/UCpHTAE2EOwWkWGnW2HY8gRw/join ติดตาม #VoiceTV YouTube : https://www.youtube.com/channel/UCpHTAE2EOwWkWGnW2HY8gRw Facebook : https://www.facebook.com/pg/VoiceTVRankingThailand Instagram : https://www.instagram.com/voicetv/ Twitter : https://twitter.com/VoiceTVOfficial Website : https://www.voicetv.co.th/ Source: https://www.youtube.com/watch?v=fUrtterb4Z0 Uploader: VOICE TV

#มองโลกมองไทย ประจำวันที่ 25 ตุลาคม 2563 คุยกับคอมพิวเตอร์ด้วยภาษามนุษย์? ปราบ เลาหะโรจนพันธ์ อาจารย์พิเศษ วิชา Machine Learning ม.ธรรมศาสตร์ แขกรับเชิญพิเศษจะมาเล่าให้ฟังว่า 'ทักษะภาษาคอมพ์ฯ' ช่วยให้เราพลิกวิธีคิด พลิกอนาคตอย่างไร และทำไม โลกถึงนิยม และมองหาคนที่มีทักษะนี้เสียเหลือเกิน? Machine Learning สำหรับมนุษย์เงินเดือน โดย ThinkLab Creative Space & Cafe https://www.facebook.com/thinklab.creativespace/posts/194751758721175 พิเศษ! เพียงระบุว่ารับชมมาจากรายการ "มองโลก มองไทย" รับส่วนลด 10% Facebook Page: ThinkLab Creative Space and Cafe โทรศัพท์/ LINE ID: 0610247199 สมัครสมาชิกเพื่อรับสิทธิพิเศษ (Membership) https://www.youtube.com/channel/UCpHTAE2EOwWkWGnW2HY8gRw/join ติดตาม #VoiceTV YouTube : https://www.youtube.com/channel/UCpHTAE2EOwWkWGnW2HY8gRw Facebook : https://www.facebook.com/pg/VoiceTVRankingThailand Instagram : https://www.instagram.com/voicetv/ Twitter : https://twitter.com/VoiceTVOfficial Website : https://www.voicetv.co.th/ Source: https://www.youtube.com/watch?v=fUrtterb4Z0 Uploader: VOICE TV

#มองโลกมองไทย ประจำวันที่ 25 ตุลาคม 2563 คุยกับคอมพิวเตอร์ด้วยภาษามนุษย์? ปราบ เลาหะโรจนพันธ์ อาจารย์พิเศษ วิชา Machine Learning ม.ธรรมศาสตร์ แขกรับเชิญพิเศษจะมาเล่าให้ฟังว่า 'ทักษะภาษาคอมพ์ฯ' ช่วยให้เราพลิกวิธีคิด พลิกอนาคตอย่างไร และทำไม โลกถึงนิยม และมองหาคนที่มีทักษะนี้เสียเหลือเกิน? Machine Learning สำหรับมนุษย์เงินเดือน โดย ThinkLab Creative Space & Cafe https://www.facebook.com/thinklab.creativespace/posts/194751758721175 พิเศษ! เพียงระบุว่ารับชมมาจากรายการ "มองโลก มองไทย" รับส่วนลด 10% Facebook Page: ThinkLab Creative Space and Cafe โทรศัพท์/ LINE ID: 0610247199 สมัครสมาชิกเพื่อรับสิทธิพิเศษ (Membership) https://www.youtube.com/channel/UCpHTAE2EOwWkWGnW2HY8gRw/join ติดตาม #VoiceTV YouTube : https://www.youtube.com/channel/UCpHTAE2EOwWkWGnW2HY8gRw Facebook : https://www.facebook.com/pg/VoiceTVRankingThailand Instagram : https://www.instagram.com/voicetv/ Twitter : https://twitter.com/VoiceTVOfficial Website : https://www.voicetv.co.th/ Source: https://www.youtube.com/watch?v=fUrtterb4Z0 Uploader: VOICE TV

#มองโลกมองไทย ประจำวันที่ 25 ตุลาคม 2563 คุยกับคอมพิวเตอร์ด้วยภาษามนุษย์? ปราบ เลาหะโรจนพันธ์ อาจารย์พิเศษ วิชา Machine Learning ม.ธรรมศาสตร์ แขกรับเชิญพิเศษจะมาเล่าให้ฟังว่า 'ทักษะภาษาคอมพ์ฯ' ช่วยให้เราพลิกวิธีคิด พลิกอนาคตอย่างไร และทำไม โลกถึงนิยม และมองหาคนที่มีทักษะนี้เสียเหลือเกิน? Machine Learning สำหรับมนุษย์เงินเดือน โดย ThinkLab Creative Space & Cafe https://www.facebook.com/thinklab.creativespace/posts/194751758721175 พิเศษ! เพียงระบุว่ารับชมมาจากรายการ "มองโลก มองไทย" รับส่วนลด 10% Facebook Page: ThinkLab Creative Space and Cafe โทรศัพท์/ LINE ID: 0610247199 สมัครสมาชิกเพื่อรับสิทธิพิเศษ (Membership) https://www.youtube.com/channel/UCpHTAE2EOwWkWGnW2HY8gRw/join ติดตาม #VoiceTV YouTube : https://www.youtube.com/channel/UCpHTAE2EOwWkWGnW2HY8gRw Facebook : https://www.facebook.com/pg/VoiceTVRankingThailand Instagram : https://www.instagram.com/voicetv/ Twitter : https://twitter.com/VoiceTVOfficial Website : https://www.voicetv.co.th/ Source: https://www.youtube.com/watch?v=fUrtterb4Z0 Uploader: VOICE TV

289+ Machine Learning Projects With Python Code

This is a beginner level book.