📐 ML Fundamentals

Neural networks, backpropagation, gradient descent — the maths behind AI

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Learn TensorFlow and Deep Learning fundamentals with Python (code-first introduction) Part 1/2

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TensorFlow DCGAN Tutorial

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MLOps Live Community Session Announcement

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L11.7 Weight Initialization in PyTorch -- Code Example

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L11.6 Xavier Glorot and Kaiming He Initialization

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Expectations with Machine Learning

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Machine Learning Frameworks - The Landscape

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L11.5 Weight Initialization -- Why Do We Care?

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L11.4 Why BatchNorm Works

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L11.3 BatchNorm in PyTorch -- Code Example

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L11.2 How BatchNorm Works

Sebastian Raschka Beginner 5y ago

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L11.1 Input Normalization

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L11.0 Input Normalization and Weight Initialization -- Lecture Overview

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L10.5.4 Dropout in PyTorch

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L10.5.3 (Optional) Dropout Ensemble Interpretation

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L10.5.2 Dropout Co-Adaptation Interpretation

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L10.5.1 The Main Concept Behind Dropout

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L10.4 L2 Regularization for Neural Nets

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L10.3 Early Stopping

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L10.2 Data Augmentation in PyTorch

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L10.1 Techniques for Reducing Overfitting

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📚 Coursera Courses Opens on Coursera · Free to audit

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Algorithmic Thinking (Part 1)

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IA Para Todos (Português)

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TensorFlow: Build & Deploy Face Mask Detection

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Predictive Analytics

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Machine Learning: an overview

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Machine Learning Fundamentals

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AWS Artificial Intelligence Practitioner

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Preparing Images for AI Models

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Managing Machine Learning Models

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Smart Analytics, Machine Learning, and AI on Google Cloud

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Machine Learning with PyTorch and Scikit-Learn

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Geometric Algorithms

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Responsible AI: Applying AI Principles with Google Cloud

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Deep Learning with Keras and Tensorflow

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Mask Detector with Python & TensorFlow: Build & Deploy

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Python: Implement & Evaluate Random Forests for ML

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Pengantar AI dan Machine Learning di Google Cloud

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IoT (Internet of Things) Wireless & Cloud Computing Emerging Technologies

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