{"id":28658,"date":"2023-11-17T21:24:43","date_gmt":"2023-11-17T15:54:43","guid":{"rendered":"https:\/\/tocxten.com\/?page_id=28658"},"modified":"2023-11-19T17:55:46","modified_gmt":"2023-11-19T12:25:46","slug":"convolution-neural-network","status":"publish","type":"page","link":"https:\/\/tocxten.com\/index.php\/convolution-neural-network\/","title":{"rendered":"Convolution Neural Network"},"content":{"rendered":"\n<p class=\"has-large-font-size\"><strong>Contents<\/strong><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Chapter 1: Introduction to Neural Networks<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>1.1 <a href=\"https:\/\/tocxten.com\/index.php\/overview-of-neural-networks\/\">Overview of Neural Networks<\/a><\/li>\n\n\n\n<li>1.2 Historical Perspective<\/li>\n\n\n\n<li>1.3 Types of Neural Networks<\/li>\n\n\n\n<li>1.4 Motivation for CNNs<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Chapter 2: Basics of Convolutional Neural Networks<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>2.1 Fundamentals of Neural Networks<\/li>\n\n\n\n<li>2.2 Convolutional Layers<\/li>\n\n\n\n<li>2.3 Pooling Layers<\/li>\n\n\n\n<li>2.4 Fully Connected Layers<\/li>\n\n\n\n<li>2.5 Activation Functions<\/li>\n\n\n\n<li>2.6 Training CNNs<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Chapter 3: Convolutional Layers<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>3.1 Convolutional Operation<\/li>\n\n\n\n<li>3.2 Filters and Kernels<\/li>\n\n\n\n<li>3.3 Padding and Stride<\/li>\n\n\n\n<li>3.4 Feature Maps<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Chapter 4: Pooling Layers<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>4.1 Max Pooling<\/li>\n\n\n\n<li>4.2 Average Pooling<\/li>\n\n\n\n<li>4.3 Global Average Pooling<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Chapter 5: Fully Connected Layers<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>5.1 Dense Layers<\/li>\n\n\n\n<li>5.2 Role in CNNs<\/li>\n\n\n\n<li>5.3 Output Layers<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Chapter 6: Architectures of CNNs<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>6.1 LeNet-5<\/li>\n\n\n\n<li>6.2 AlexNet<\/li>\n\n\n\n<li>6.3 VGGNet<\/li>\n\n\n\n<li>6.4 GoogLeNet (Inception)<\/li>\n\n\n\n<li>6.5 ResNet<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Chapter 7: Transfer Learning with CNNs<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>7.1 Pre-trained Models<\/li>\n\n\n\n<li>7.2 Fine-tuning<\/li>\n\n\n\n<li>7.3 Practical Considerations<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Chapter 8: Object Detection and Localization<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>8.1 Region-based CNNs (R-CNN)<\/li>\n\n\n\n<li>8.2 Faster R-CNN<\/li>\n\n\n\n<li>8.3 You Only Look Once (YOLO)<\/li>\n\n\n\n<li>8.4 Single Shot MultiBox Detector (SSD)<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Chapter 9: Image Segmentation<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>9.1 Semantic Segmentation<\/li>\n\n\n\n<li>9.2 Instance Segmentation<\/li>\n\n\n\n<li>9.3 U-Net Architecture<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Chapter 10: Applications of CNNs<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>10.1 Image Classification<\/li>\n\n\n\n<li>10.2 Image Generation<\/li>\n\n\n\n<li>10.3 Medical Imaging<\/li>\n\n\n\n<li>10.4 Autonomous Vehicles<\/li>\n\n\n\n<li>10.5 Natural Language Processing (NLP) and CNNs<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Chapter 11: Challenges and Future Directions<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>11.1 Current Challenges<\/li>\n\n\n\n<li>11.2 Recent Advancements<\/li>\n\n\n\n<li>11.3 Future Trends<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Chapter 12: Case Studies<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>12.1 Real-world Applications<\/li>\n\n\n\n<li>12.2 Success Stories<\/li>\n\n\n\n<li>12.3 Lessons Learned<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Chapter 13: Conclusion<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>13.1 Summary<\/li>\n\n\n\n<li>13.2 Final Thoughts<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Contents Chapter 1: Introduction to Neural Networks Chapter 2: Basics of Convolutional Neural Networks Chapter 3: Convolutional Layers Chapter 4: Pooling Layers Chapter 5: Fully Connected Layers Chapter 6: Architectures&#8230;<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":"","_links_to":"","_links_to_target":""},"class_list":["post-28658","page","type-page","status-publish","hentry"],"post_mailing_queue_ids":[],"_links":{"self":[{"href":"https:\/\/tocxten.com\/index.php\/wp-json\/wp\/v2\/pages\/28658","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/tocxten.com\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/tocxten.com\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/tocxten.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/tocxten.com\/index.php\/wp-json\/wp\/v2\/comments?post=28658"}],"version-history":[{"count":2,"href":"https:\/\/tocxten.com\/index.php\/wp-json\/wp\/v2\/pages\/28658\/revisions"}],"predecessor-version":[{"id":28665,"href":"https:\/\/tocxten.com\/index.php\/wp-json\/wp\/v2\/pages\/28658\/revisions\/28665"}],"wp:attachment":[{"href":"https:\/\/tocxten.com\/index.php\/wp-json\/wp\/v2\/media?parent=28658"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}