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What are you focused on learning right now? From Rust and Python to Italian, here's what our community of maintainers are educating themselves on. Read more of their stories here: https://t.co/0fmuxQyYM2 https://t.co/VwfH6YUjSX Source: https://twitter.com/github/status/1555626135781875712 Uploader: GitHub

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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

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 wget https://archive.org/download/github.com-fchollet-deep-learning-with-python-notebooks_-_2019-12-15_19-21-33/fchollet-deep-learning-with-python-notebooks_-_2019-12-15_19-21-33.bundle and run: git clone fchollet-deep-learning-with-python-notebooks_-_2019-12-15_19-21-33.bundle Source: https://github.com/fchollet/deep-learning-with-python-notebooks Uploader: fchollet Upload date: 2019-12-15

People talk about a Moore's Law for gene sequencing, a Moore's Law for software, etc. This is talk is about *the* Moore's Law, the bull that the other "Laws" ride; and how Python-powered ML helps drive it. How do we keep making ever-smaller devices? How do we harness atomic-scale physics? Large-scale machine learning is key. The computation drives new chip designs, and those new chip designs are used for new computations, ad infinitum. High-dimensional regression, classification, active learning, optimization, ranking, clustering, density estimation, scientific visualization, massively parallel processing -- it all comes into play, and Python is powering it all.

learning programming with python in persian

Practical Machine Learning By Example In Python

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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/

Deep Learning With Python

Deep Learning With Python

Deep Learning With Python

Deep Learning With Python

Introduction-to-machine-learning-with-python-a-guide for data scientists by andreas-c.-muller-sarah-guido

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Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (08 - Polynomial Regression)

Machine Learning A-Z AI, Python & R + ChatGPT Bonus 2023 (08 - Polynomial Regression)

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.

Learn Python from start to end

Welcome to the world of network programming with Python. Python is a full-featured object-oriented programming language with a standard library that includes everything needed to rapidly build powerful network applications. In addition, it has a multitude of third-party libraries and packages that extend Python to every sphere of network programming. Combined with the fun of using Python, with this book, we hope to get you started on your journey so that you master these tools and produce some great networking code. In this book, we are squarely targeting Python 3. Although Python 3 is still establishing itself as the successor to Python 2, version 3 is the future of the language, and we want to demonstrate that it is ready for network programming prime time. It offers many improvements over the previous version, many of which improve the network programming experience, with enhanced standard library modules and new additions. We hope you enjoy this introduction to network programming with Python. Dr. M. O. Faruque Sarker    Sam Washington

Learning Professional Python 1

Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.

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

Imbalanced-learn is an open-source python toolbox aiming at providing a wide range of methods to cope with the problem of imbalanced dataset frequently encountered in machine learning and pattern recognition. The implemented state-of-the-art methods can be categorized into 4 groups: (i) under-sampling, (ii) over-sampling, (iii) combination of over- and under-sampling, and (iv) ensemble learning methods. The proposed toolbox only depends on numpy, scipy, and scikit-learn and is distributed under MIT license. Furthermore, it is fully compatible with scikit-learn and is part of the scikit-learn-contrib supported project. Documentation, unit tests as well as integration tests are provided to ease usage and contribution. The toolbox is publicly available in GitHub: https://github.com/scikit-learn-contrib/imbalanced-learn.

Imbalanced-learn is an open-source python toolbox aiming at providing a wide range of methods to cope with the problem of imbalanced dataset frequently encountered in machine learning and pattern recognition. The implemented state-of-the-art methods can be categorized into 4 groups: (i) under-sampling, (ii) over-sampling, (iii) combination of over- and under-sampling, and (iv) ensemble learning methods. The proposed toolbox only depends on numpy, scipy, and scikit-learn and is distributed under MIT license. Furthermore, it is fully compatible with scikit-learn and is part of the scikit-learn-contrib supported project. Documentation, unit tests as well as integration tests are provided to ease usage and contribution. The toolbox is publicly available in GitHub: https://github.com/scikit-learn-contrib/imbalanced-learn.

Imbalanced-learn is an open-source python toolbox aiming at providing a wide range of methods to cope with the problem of imbalanced dataset frequently encountered in machine learning and pattern recognition. The implemented state-of-the-art methods can be categorized into 4 groups: (i) under-sampling, (ii) over-sampling, (iii) combination of over- and under-sampling, and (iv) ensemble learning methods. The proposed toolbox only depends on numpy, scipy, and scikit-learn and is distributed under MIT license. Furthermore, it is fully compatible with scikit-learn and is part of the scikit-learn-contrib supported project. Documentation, unit tests as well as integration tests are provided to ease usage and contribution. The toolbox is publicly available in GitHub: https://github.com/scikit-learn-contrib/imbalanced-learn.

#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

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

Supervised Machine Learning with Python. Classification: Support Vector Machines

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Want to master Machine Learning in Python? In this video, we break down the Top 10 Python Libraries every ML & AI practitioner should know! These libraries are essential for data preprocessing, model building, deep learning, and performance optimization. 🔹 Scikit-Learn – The go-to library for ML models 🤖 🔹 TensorFlow & PyTorch – Powering Deep Learning & Neural Networks 🔥 🔹 XGBoost & LightGBM – Boosting algorithms for high-performance ML 📈 🔹 Pandas & NumPy – Data manipulation & numerical computing 📊 🔹 Matplotlib & Seaborn – Data visualization & insights 📉 🔹 NLTK & SpaCy – NLP & text analytics 📝 ✅ Want to become a Data Science Expert? Explore the best Data Science Certification Course in Delhi and take your ML skills to the next level! For more information visit our website: https://bostoninstituteofanalytics.org/india/delhi/connaught-place/school-of-technology-ai/data-science-and-artificial-intelligence/

Ce tutoriel python francais vous présente SKLEARN, le meilleur package pour faire du machine learning avec Python. Tous les modèles, et tous les algorithmes de machine learning ont déjà été implémentés avec une architecture orientée objet, chaque modèle disposant de sa propre classe. KNN, LinearRegression, Decision Trees, Support vector machines, etc. Pour créer un modèle, on génère un objet de la classe correspondante. Au passage, c’est ce qu’on appelle un estimateur (dans sklearn) On peut aussi préciser entre parenthèse les hyper-paramètres de notre modèle. Par exemple, le learning rate d’une descente de gradient, ou bien le nombre d’arbres dans une Random Forest. Une fois qu’on a initialisé notre modèle, on va pouvoir entraîner, l’évaluer, et l’utiliser grâce a trois méthodes qu’on retrouve dans toutes les classes de Sklearn. Ce sont les méthodes Fit, score et predict. ► EXEMPLE Régression Linéaire from sklearn.linear_model import LinearRegression model = LinearRegression() model.fit(X, y) model.score(X, y) model.predict(X) ► EXEMPLE K-Nearest Neighbors from sklearn.neighbors import KNeighborClassifier model = KNeighborsClassifier() model.fit(X, y) model.score(X, y) model.predict(X) ► TIMECODE DE LA VIDEO: 0:00 : Intro 01:00 : Comprendre le Machine Learning et L'apprentissage supervisé 04:25 : SKLEARN, API, et le fonctionnement d'un estimateur 08:27 : Régression avec SKLEARN (LinearRegression + SVR) 11:47 : Classification avec SKLEARN (K-Nearest Neighbor) 15:30 : Auriez-vous survécu au TItanic ? + Exercice SKLEARN https://scikit-learn.org/stable/ Carte des algorithmes de SKLEARN https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html ► Me soutenir financièrement sur Tipeee ou Utip (et obtenir des vidéos BONUS) https://fr.tipeee.com/machine-learnia https://utip.io/machinelearnia/ ► MON SITE INTERNET EN COMPLÉMENT DE CETTE VIDÉO: https://machinelearnia.com/ ► 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/ ► 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]

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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]

Machine Learning (ML) is a subfield of Artificial Intelligence that gives computers the ability to learn from past data without being explicitly programmed. The predictive capabilities of ML models have already been used to facilitate several scientific breakthroughs. However, the practical application of ML is often limited due to the gaps in technical knowledge of its users. The common issue faced by many scientific researchers is the inability to choose the appropriate ML pipelines that are needed to treat real-world data, which is often sparse and noisy. To solve this problem, we have developed an automated Machine Learning tool (MLtool) that includes a set of ML algorithms and approaches to aid scientific researchers. The current version of MLtool is implemented as an object-oriented Python code that is easily extensible. It includes 44 different regression algorithms used to model data. MLtool helps users select the best model for their data, based on the scoring metrics used. Besides regression algorithms, MLtool also includes a suite of pre- and post-processing techniques such as missing value imputation, categorical variable encoding, input feature normalization, uncertainty quantification, exploratory data analysis (EDA), etc. MLtool was tested on several publicly available multi-dimensional data sets and was found capable of making accurate predictions.

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.

Learning Python

Perma.cc archive of https://easternbloc.ca/en/lab/workshops/machinelearning2021-eng created on 2021-06-08 18:43:02+00:00.

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.