Introduction to Deep Learning - MIT 2018
Explore foundations of deep learning, including perceptrons, neural networks, loss functions, and optimization techniques. Gain insights into training challenges and strategies for effective model development.
PyTorch Tutorials 神经网络 教学
深入探讨PyTorch神经网络：从基础概念到高级应用，涵盖CNN、RNN、自编码器、强化学习和GAN，并讲解优化技巧如批标准化和过拟合处理。
PyTorch
PyTorch ile derin öğrenme: Temel kavramlardan CNN'lere kadar kapsamlı bir eğitim. Yapay sinir ağları, lineer regresyon, aktivasyon fonksiyonları ve optimizasyon teknikleri üzerine uygulamalı dersler.
Keras ile Derin Öğrenme
Keras kullanarak derin öğrenme tekniklerini öğrenin. Kanser tespitinden görüntü işlemeye, oyun yapay zekasından LSTM'e kadar çeşitli uygulamalarla pratik yapın.
Deep learning ด้วย tensorflow และ keras
เรียนรู้การสร้างโมเดล Deep Learning ด้วย TensorFlow และ Keras ตั้งแต่พื้นฐานจนถึงระดับสูง ครอบคลุมการติดตั้ง การใช้งาน และเทคนิคการปรับแต่งโมเดล
TensorFlow Tutorials 神经网络 教学 教程
从零开始学习TensorFlow构建神经网络，涵盖基础概念、CNN、RNN等高级主题，通过实例和可视化深入理解深度学习原理和应用。
Tensorflow Tutorial for Beginner in Python - Hindi
हिंदी में TensorFlow की शुरुआत से लेकर उन्नत अवधारणाओं तक का व्यापक प्रशिक्षण, जिसमें न्यूरल नेटवर्क, रिग्रेशन, वर्गीकरण और CNN शामिल हैं।
Constructing Physics-Consistent Neural Networks Using Probability Theory
Explore the link between probability, physics, and neural networks, focusing on constructing physics-consistent models using Gaussian processes and the central limit theorem.
Dedicated Audio Processors at the Edge Are the Future of AI
Explore dedicated audio processors for edge AI, improving user interfaces with lower latency and cost. Learn about voice integration challenges and design capabilities for effective implementation.
Deep Learning with the Analytical Engine
Explore the potential of the Analytical Engine for deep learning, blending 19th-century mechanical computing with modern AI concepts in this Victorian-era adventure.
Python Neural Networks - Tensorflow 2.0 Tutorial - What Is a Neural Network?
Explore neural networks using TensorFlow 2.0, covering key concepts, architecture, and practical implementation in Python for beginners and intermediate learners.
Machine Learning: Neural Networks and Deep Learning - Lecture 18
Dive into advanced data science concepts through comprehensive coverage of machine learning principles, algorithms, and practical applications in this university-level lecture.
Machine Learning Fundamentals - Lecture 16
Dive into advanced data science concepts through comprehensive coverage of machine learning principles, algorithms, and practical applications in this university-level lecture.
Machine Learning Fundamentals - Lecture 15
Dive into advanced data science concepts through comprehensive coverage of machine learning principles, algorithms, and practical applications in this university-level lecture.
Machine Learning Fundamentals - Lecture 14
Dive into advanced machine learning concepts and techniques through comprehensive coverage of key algorithms, theoretical foundations, and practical applications in data science.
Machine Learning Fundamentals - Lecture 13
Dive into advanced machine learning concepts and techniques through comprehensive coverage of key algorithms, theoretical foundations, and practical applications in data science.
Machine Learning Fundamentals - Lecture 12
Dive into advanced machine learning concepts and data science fundamentals through comprehensive theoretical discussions and practical applications in this graduate-level lecture.
Machine Learning: Neural Networks and Deep Learning - Lecture 21
Dive into advanced data science concepts through comprehensive coverage of machine learning principles, algorithms, and practical applications in this graduate-level academic lecture.
Machine Learning - Lecture 20
Dive into advanced data science concepts through comprehensive coverage of machine learning principles, algorithms, and practical applications in this university-level lecture.
Machine Learning - Lecture 19
Dive into advanced data science concepts through comprehensive coverage of machine learning principles, algorithms, and practical applications in this university-level lecture.
Introduction to Neural Networks - Lecture 25
Explore neural networks' fundamental concepts, structure, and prediction mechanisms while learning essential training approaches for building effective machine learning models.