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Showing posts from January, 2019
Introduction to Deep Learning with Keras and Tensorflow || Rodrigo Agundez
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Machine Learning Tutorial Python - 4: Gradient Descent and Cost Function
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Machine Learning with Scikit-Learn and Xgboost on Google Cloud Platform ...
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Deep Learning(CS7015): Lec 6.4 Principal Component Analysis and its Inte...
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Principal Component Analysis (PCA): An introduction for non-specialists-...
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Applied AI Course: Productionization and deployment of Machine Learning ...
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Illustrated Guide to Recurrent Neural Networks: Understanding the Intuition
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Amazing Way to Graph the Gradient Function (Derivative)
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Deep Learning Basics: Introduction and Overview - MIT
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Gradient Descent Vs Ordinary Least Square (OLS) estimation
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How to find the best model parameters in scikit-learn
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Decision Tree Important Points ll Machine Learning ll DMW ll Data Analyt...
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Support Vector Machine (SVM) - Fun and Easy Machine Learning
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Model Evaluation: Introduction to the Cross Validation and Hold-out methods
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Let’s Write a Decision Tree Classifier from Scratch - Machine Learning R...
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Let’s Write a Decision Tree Classifier from Scratch - Machine Learning R...
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Deep Learning Prerequisites: The Numpy Stack in Python
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Four Types Of Cross Validation| K-Fold | Leave One Out |Bootstrap | Hold...
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Deep Learning: What is it good for? - Prof. Ankit Patel - Rice University
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Machine Learning Tutorial - Parameter Tuning with Python and scikit-learn
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Machine Learning Tutorial Python - 5: Save Model Using Joblib And Pickle
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Gradient descent, how neural networks learn | Deep learning, chapter 2
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Regression I: What is regression? | SSE, SSR, SST | R-squared | Errors (...
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Neural Networks Demystified [Part 2: Forward Propagation]
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