Blog: Review: DilatedNet - Dilated Convolution (Semantic Segmentation) by Sik-Ho Tsang. Blog: Type of convolutions: Deformable and Transformable Convolution by Ali Raza. Blog: Depth wise Separable Convolutional Neural Networks by Mayank Chaurasia. Blog: A Basic Introduction to Separable Convolutions by Chi-Feng Wang. Blog: Maxpooling vs Minpooling vs Average Pooling by Madhushree Basavarajaiah. Blog: Review: Spatial Pyramid Pooling by Sanchit Tanwar. Blog: Global Average Pooling Layers for Object Localization by Alexis Cook. Blog: A Gentle Introduction to 1×1 Convolutions to Manage Model Complexity by Jason Brownlee. Blog: A Comprehensive Guide to Convolutional Neural Networks - the ELI5 Way by Sumit Saha. Blog: Understanding Convolutions by Christopher Olah. Blog: Convolutional Neural Networks CheatSheet by Afshine Amidi and Shervine Amidi. Slides: Optimization for Training Deep Models 1 and 2 by U Kang. Slide: Training Deep Neural Networks by Aykut Erdem. Slide: Conjugate Gradient Descent by Aarti Singh. Slide: Optimization for Training Deep Models - Algorithms (Lecture 4) by Ali Harakeh. Slide: Optimization for Training Deep Models (Lecture 4) by Ali Harakeh. Blog: A (Slightly) Better Budget Allocation for Hyperband by Alexandre Abrahamīlog: Massively Parallel Hyperparameter Optimization by Liam Li. Part 3: Contrastive Divergence Algorithm by Nguyễn Văn Lĩnh Blog: Restricted Boltzmann Machine, a Complete Analysis. Paper: Understanding the Disharmony between Dropout and Batch Normalization by Variance Shift by Xiang Li, Shuo Chen, Xiaolin Hu, and Jian Yang. Blog: Neural Network Optimization by Matthew Stewart. Paper: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Blog: Preconditioning the Network by Nic Schraudolph and Fred Cummins. Video of lecture / discussion: This video covers a presentation by Ian Goodfellow and group discussion on the end of Chapter 8 and entirety of Chapter 9 at a reading group in San Francisco organized by Taro-Shigenori Chiba. Blog: Understanding the Backward Pass Through Batch Normalization Layer by Frederik Kratzert. Blog: Why Momentum Really Works by Gabriel Goh. Fascinating Guides For Machine Learning. Lecture 19: Atention Mechanisms and Transformers. Lecture 17: Generative Adversarial Networks. #IAN GOODFELLOW DEEP LEARNING PDF SOFTWARE#
Lecture 14: Toolkit Lab 7: Optimization Software (Ray Tune or Optuna).Lecture 12: Toolkit Lab 6: Transfer Learning and Other Tricks.Lecture 11: Sequence Modeling: Recurrent and Recursive Networks.Lecture 10: Toolkit Lab 5: Using Convolutions to Generalize.
Lecture 8: Optimization for Training Deep Models.Lecture 7: Toolkit Lab 4: Using a Neural Network to Fit the Data with PyTorch.Lecture 6: Regularization for Deep Learning.Lecture 5: Toolkit Lab 3: Preprocessing Datasets by PyTorch.
Lecture 3: Toolkit Lab 2: Getting Started with PyTorch.Lecture 2: Toolkit Lab 1: Google Colab and Anaconda.
Deep Learning Course: Deep Learning View on GitHubĭeep Learning Using PyTorch Lecturer: Hossein Hajiabolhassan
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