Liked on YouTube: Logistic Regression & SoftMax Regression | Machine Learning with TensorFlow & scikit-learn #12
Logistic Regression & SoftMax Regression | Machine Learning with TensorFlow & scikit-learn #12
📚About This lecture shows straightforward python implementations of both Logistic regression and its generalized version, the SoftMax Regression, that is used for multi-class classification. ⏲Outline⏲ 00:00 Introduction 00:01 Logistic Regression 20:20 SoftMax Regression 29:48 Outro 🔴 Subscribe for more videos on Machine Learning and Python. 👍 Smash that like button, in case you find this tutorial useful. 👁🗨 Speak up and comment, I am all ears. ============================================================ Lecture 1: Introduction https://youtu.be/yeTAlrhdzhc Lecture 2: Binary Classification & SGD Classifier https://youtu.be/aXpsCyXXMJE Lecture 3: Performance Measures https://youtu.be/UA_ZAwPVLxg Lecture 4: Multiclass classification & Cross Validation https://youtu.be/5KyH6v8oKNQ Lecture 5: Gradient Descent https://youtu.be/OWM0wMtUhME Lecture 6: Multilabel and Multioutput Classification https://youtu.be/bDdjebakjbA Lecture 7: Linear Regression with Louis from "What is Artificial Intelligence" https://youtu.be/JWQJMoDC9hg Lecture 8: Polynomial Regression feat. Luis Serrano & YouTube's Video Recommendation Algorithm https://youtu.be/HmmkA-EFaW0 Lecture 9: Simulated Annealing x SGD x Mini-batch https://youtu.be/3xJ4-2LUiHU Lecture 10: Ridge Regression https://youtu.be/PtBuqAdbpfY Lecture 11: LASSO Regression and Elastic-Net Regression https://youtu.be/kNiYiUiW8dY Logistic Regression for newcomers: https://youtu.be/R-gJeIZ11zU ============================================================ Instructor: Dr. Ahmad Bazzi IG: https://ift.tt/2CoMhMW Browser: https://ift.tt/zZ5dqW ============================================================ Credits: Google https://www.google.com/ Google Photos https://ift.tt/1Duo12L TensorFlow https://ift.tt/1Xwlwg0 scikit-learn https://ift.tt/2L0QB8Q Numpy https://numpy.org/ Microsoft OneNote https://ift.tt/2I0UkRN Python https://www.python.org/ ============================================================ References: [1] Géron, Aurélien. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O'Reilly Media, 2019. https://ift.tt/2QouKeF [2] Bishop, Christopher M. Pattern recognition and machine learning. springer, 2006. https://ift.tt/2oEiur8 [3] Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. The elements of statistical learning. Vol. 1. No. 10. New York: Springer series in statistics, 2001. https://ift.tt/2oVPAWt [4] Burkov, Andriy. The hundred-page machine learning book. Quebec City, Can.: Andriy Burkov, 2019. https://ift.tt/3aWS5Og [5] Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016. https://ift.tt/2bWPI0L [6] Chollet, Francois. Deep Learning mit Python und Keras: Das Praxis-Handbuch vom Entwickler der Keras-Bibliothek. MITP-Verlags GmbH & Co. KG, 2018. https://ift.tt/3jgc4dI [7] De Prado, Marcos Lopez. Advances in financial machine learning. John Wiley & Sons, 2018. https://ift.tt/2ILDdUd [8] Duda, Richard O., Peter E. Hart, and David G. Stork. Pattern classification. John Wiley & Sons, 2012. https://ift.tt/2kLfcnv [9] Lapan, Maxim. Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more. Packt Publishing Ltd, 2018. https://ift.tt/3aZuT1B [10] Bonaccorso, Giuseppe. Machine Learning Algorithms: Popular algorithms for data science and machine learning. Packt Publishing Ltd, 2018. https://ift.tt/2EgwVyI [11] Deisenroth, Marc Peter, A. Aldo Faisal, and Cheng Soon Ong. Mathematics for machine learning. Cambridge University Press, 2020. https://ift.tt/2HX9Vor [12] Krollner, Bjoern, Bruce J. Vanstone, and Gavin R. Finnie. "Financial time series forecasting with machine learning techniques: a survey." ESANN. 2010. #MachineLearning #TensorFlow #MachineLearningTutorial
via YouTube https://www.youtube.com/watch?v=JJFT5kLBUjg
📚About This lecture shows straightforward python implementations of both Logistic regression and its generalized version, the SoftMax Regression, that is used for multi-class classification. ⏲Outline⏲ 00:00 Introduction 00:01 Logistic Regression 20:20 SoftMax Regression 29:48 Outro 🔴 Subscribe for more videos on Machine Learning and Python. 👍 Smash that like button, in case you find this tutorial useful. 👁🗨 Speak up and comment, I am all ears. ============================================================ Lecture 1: Introduction https://youtu.be/yeTAlrhdzhc Lecture 2: Binary Classification & SGD Classifier https://youtu.be/aXpsCyXXMJE Lecture 3: Performance Measures https://youtu.be/UA_ZAwPVLxg Lecture 4: Multiclass classification & Cross Validation https://youtu.be/5KyH6v8oKNQ Lecture 5: Gradient Descent https://youtu.be/OWM0wMtUhME Lecture 6: Multilabel and Multioutput Classification https://youtu.be/bDdjebakjbA Lecture 7: Linear Regression with Louis from "What is Artificial Intelligence" https://youtu.be/JWQJMoDC9hg Lecture 8: Polynomial Regression feat. Luis Serrano & YouTube's Video Recommendation Algorithm https://youtu.be/HmmkA-EFaW0 Lecture 9: Simulated Annealing x SGD x Mini-batch https://youtu.be/3xJ4-2LUiHU Lecture 10: Ridge Regression https://youtu.be/PtBuqAdbpfY Lecture 11: LASSO Regression and Elastic-Net Regression https://youtu.be/kNiYiUiW8dY Logistic Regression for newcomers: https://youtu.be/R-gJeIZ11zU ============================================================ Instructor: Dr. Ahmad Bazzi IG: https://ift.tt/2CoMhMW Browser: https://ift.tt/zZ5dqW ============================================================ Credits: Google https://www.google.com/ Google Photos https://ift.tt/1Duo12L TensorFlow https://ift.tt/1Xwlwg0 scikit-learn https://ift.tt/2L0QB8Q Numpy https://numpy.org/ Microsoft OneNote https://ift.tt/2I0UkRN Python https://www.python.org/ ============================================================ References: [1] Géron, Aurélien. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O'Reilly Media, 2019. https://ift.tt/2QouKeF [2] Bishop, Christopher M. Pattern recognition and machine learning. springer, 2006. https://ift.tt/2oEiur8 [3] Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. The elements of statistical learning. Vol. 1. No. 10. New York: Springer series in statistics, 2001. https://ift.tt/2oVPAWt [4] Burkov, Andriy. The hundred-page machine learning book. Quebec City, Can.: Andriy Burkov, 2019. https://ift.tt/3aWS5Og [5] Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016. https://ift.tt/2bWPI0L [6] Chollet, Francois. Deep Learning mit Python und Keras: Das Praxis-Handbuch vom Entwickler der Keras-Bibliothek. MITP-Verlags GmbH & Co. KG, 2018. https://ift.tt/3jgc4dI [7] De Prado, Marcos Lopez. Advances in financial machine learning. John Wiley & Sons, 2018. https://ift.tt/2ILDdUd [8] Duda, Richard O., Peter E. Hart, and David G. Stork. Pattern classification. John Wiley & Sons, 2012. https://ift.tt/2kLfcnv [9] Lapan, Maxim. Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more. Packt Publishing Ltd, 2018. https://ift.tt/3aZuT1B [10] Bonaccorso, Giuseppe. Machine Learning Algorithms: Popular algorithms for data science and machine learning. Packt Publishing Ltd, 2018. https://ift.tt/2EgwVyI [11] Deisenroth, Marc Peter, A. Aldo Faisal, and Cheng Soon Ong. Mathematics for machine learning. Cambridge University Press, 2020. https://ift.tt/2HX9Vor [12] Krollner, Bjoern, Bruce J. Vanstone, and Gavin R. Finnie. "Financial time series forecasting with machine learning techniques: a survey." ESANN. 2010. #MachineLearning #TensorFlow #MachineLearningTutorial
via YouTube https://www.youtube.com/watch?v=JJFT5kLBUjg
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