AI and Deep Learning Explained - CNNs, Transformers, GANs, YOLO, GNNs and More

AI and Deep Learning Explained - CNNs, Transformers, GANs, YOLO, GNNs and More

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L-1 Introduction to Deep Learning

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L-1 Introduction to Deep Learning

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AI and Deep Learning Explained - CNNs, Transformers, GANs, YOLO, GNNs and More

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  1. 1 L-1 Introduction to Deep Learning
  2. 2 L-2 Activation Functions in Deep Learning
  3. 3 L-3 TensorFlow Basics | Computational Graphs, Constants, Variables & Placeholders
  4. 4 L-4 Perceptron | Single Layer Neuron
  5. 5 L-5 Perceptron Implementation | Single Layer Perceptron
  6. 6 L-6 Optimizers & Learning Rate in Deep Learning
  7. 7 L-7 Implementation of Perceptron with Optimizer
  8. 8 L-8 What is Bias | Why We Use Bias in Neural Networks
  9. 9 L-9 Binary Classification Using Perceptron | Single Layer Neuron
  10. 10 L-10 Image Classification using Perceptron | Singla Layer Neuron
  11. 11 L-11 Image Classification Using Multi-Layer Perceptron (MLP) with Keras
  12. 12 L-12 Computer Vision Vs Image Processing
  13. 13 L-13 Vanishing Gradients | Deep Learning Problem & Solution
  14. 14 Convolutional Neural Network Explained | CNN | Deep Learning Tutorial
  15. 15 Master Convolutional Neural Networks | Deep Learning Image Classification Tutorial
  16. 16 Padding in CNN Explained | Zero, Same & Valid Padding with Stride Tutorial
  17. 17 LeNet Explained | Layer-by-Layer CNN Architecture Guide
  18. 18 AlexNet Explained Step by Step | Deep Learning & CNN Tutorial
  19. 19 VGG16 Tutorial | Build & Train Your Own Convolutional Neural Network
  20. 20 ResNet Explained Step by Step( Residual Networks)
  21. 21 Explained Identity Block and Convolution Block in ResNet | Residual Networks
  22. 22 ResNet Using Keras | Residual Network | Convolutional Neural Network | Deep Learning & CNN Tutorial
  23. 23 EfficientNet Explained: Rethinking Model Scaling for Convolutional Neural Networks
  24. 24 EfficientNet Implementation | EfficientNet B0 - B7 Implementation
  25. 25 EfficientNet on Custom Dataset | Image Classification Using EfficientNet
  26. 26 Training Neural Networks on GPU vs CPU | Performance Test
  27. 27 Inception Network | Inception Module | InceptionV1
  28. 28 InceptionV3 | Inception Network
  29. 29 Inception V3 Practical Implementation | InceptionV3
  30. 30 DenseNet | Densely Connected Convolutional Networks
  31. 31 DenseNet-121 Implementation on Custom Dataset | DenseNet
  32. 32 YOLO - Object Detection Using Python
  33. 33 Object Detection Using Yolo | Yolo V 3 network from scratch using Python
  34. 34 Object Detection Using YOLO v4 on Custom Dataset | Practical Implementation
  35. 35 Traffic Sign Detection Using Yolo V4 | Yolov4 on Custom Dataset
  36. 36 Object Detection Using YOLOv4-tiny | Part 1
  37. 37 Install Darknet framework | Object Detection using yolov4
  38. 38 TFLite Object Detection Android App Tutorial | Object Detection Using Yolov4 tiny
  39. 39 Yolo v5 on Custom Dataset | Train and Test Yolov5 on Custom Dataset
  40. 40 Leaf Disease Detection using yolov5 | Object Detection
  41. 41 Image Classification Using YOLOv5
  42. 42 Object Tracking Using DeepSORT and YOLOv5 | Multi Object Tracking
  43. 43 TFLite Object Detection Android App Tutorial Using YOLOv5 | Yolov5 to tflite conversion
  44. 44 YOLOR on a Custom Dataset | Object detection using YOLOR
  45. 45 YOLOv6 on Custom dataset | Object Detection
  46. 46 YOLOv6 on Custom Dataset | Object Detection
  47. 47 YOLOv7 | Object Detection | instance segmentation | keypoints detection
  48. 48 YOLOv7 | Object Detection on Custom Dataset
  49. 49 YOLOv7 | Instance Segmentation on Custom Dataset
  50. 50 Official YOLOv7 | Object Detection
  51. 51 YOLOv8 | Object Detection on a Custom Dataset using YOLOv8
  52. 52 YOLOv8 | Image Segmentation On Custom Dataset using YOLOv8
  53. 53 YOLOv8 Object Detection with Flask | Object Detection Web Application
  54. 54 Object Tracking using YOLOv8 on Custom Dataset
  55. 55 Object Tracking using YOLOv8 on Custom Dataset - Google Colab
  56. 56 Mask R-CNN Practical Implementation
  57. 57 Plant leaf disease detection using Mask R-CNN | Image Segmentation
  58. 58 Instance Segmentation Using Mask R-CNN on Custom Dataset
  59. 59 Instance Segmentation Web Application Using Mask R-CNN and FLask
  60. 60 Segment Anything Model (SAM): a new AI model from Meta AI
  61. 61 Segment Anything Model (SAM): Build Custom Image Segmentation Model Using YOLOv8 and SAM
  62. 62 Auto Annotation for generating segmentation dataset using YOLOv8 & SAM
  63. 63 Key point Detection On Custom Dataset Using YOLOv7 | YOLOv7-Pose on Custom Dataset
  64. 64 Object Detection Using YOLOv7 and Flask | Object Detection Web Application
  65. 65 Master YOLOv8 Keypoint Detection | YOLOv8-Pose | Keypoint Detection for Beginners
  66. 66 YOLO-NAS Custom Object Detection | Fall Detection Using YOLO-NAS
  67. 67 YOLO-NAS + SAM : Image Segmentation Using YOLO-NAS and Segment Anything Model
  68. 68 YOLO-NAS and StrongSORT | Object detection and tracking
  69. 69 Single Shot Detector On Custom Dataset | SSD | Object Detection Using SSD
  70. 70 Single Shot Detector On Custom Dataset | Google Colab Implementation | Object Detection
  71. 71 Single Shot Detector On Custom Dataset | Test your Custom SSD Model
  72. 72 1 Object Detection Using Faster R-CNN
  73. 73 2 Faster R-CNN | Object Detection Using Faster R-CNN
  74. 74 3 Region Proposal Network | Faster R-CNN
  75. 75 4 Region Of Interest (RoI Pooling) | Faster R-CNN | Object Detection Using Faster R-CNN
  76. 76 Faster R-CNN on Custom Dataset | Custom Object Detector
  77. 77 Single Shot Detector | SSD | Object Detection Using SSD
  78. 78 L-1 Single Shot Detector Practical Implementation
  79. 79 L-2 Single Shot Detector | Code for SSD Model
  80. 80 L-3 Single Shot Detector | Model Testing
  81. 81 Video Classification with a CNN-RNN Architecture | Human Activity Recognition
  82. 82 3D Object Detection using YOLO4 | LiDAR Dataset
  83. 83 Open3D Tutorial | 3D Data Processing | Visualize 3D Data
  84. 84 L-14 Face Detection using OpenCV
  85. 85 L-15 Face Recognition Using LBPH Face Recognizer
  86. 86 L 1 PyAudio Basics
  87. 87 L 1 NLP Basics( Tokenization, Stemming, Lemmatization, Stopwords)
  88. 88 L 2 NLP Bag Of Words(Count Vectorizer)
  89. 89 L 2 NLP Remove Punctuation
  90. 90 L 3 NLP Collacations
  91. 91 L 4 NLP Word Embedding | Natural Language Processing | word2vec
  92. 92 L 5 word2vec model train and test full code
  93. 93 L 6 RNN Introduction
  94. 94 L 6 RNN on Reuters Dataset
  95. 95 L-7 Sentiment Analysis using RNN on Custom Dataset
  96. 96 L 8 Next Word Prediction using RNN
  97. 97 L 9 Language Translator using seq2seq Model (RNN)
  98. 98 Record and Play Audios Using PyAudio
  99. 99 What Are GANs? | Generative Adversarial Networks Explained
  100. 100 Generative Adversarial Networks (GAN) - implementation in Keras
  101. 101 DCGAN | Deep Convolutional Generative Adversarial Network
  102. 102 StyleGAN Explained
  103. 103 StyleGAN Implementation
  104. 104 StyleGAN2 ADA on Custom Dataset |StyleGAN2 with adaptive discriminator augmentation (ADA)
  105. 105 StackGAN | Text to Image Generation with Stacked Generative Adversarial Networks
  106. 106 CycleGAN | Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
  107. 107 Transformers for beginners | What are they and how do they work
  108. 108 Vision Transformers explained
  109. 109 Image Classification Using Vision Transformer | ViTs
  110. 110 Vision Transformer for Image Classification Using transfer learning
  111. 111 Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
  112. 112 Image Classification Using Swin Transformer
  113. 113 Graph Neural Networks
  114. 114 Pathways Language Model | PaLM
  115. 115 Artificial General Intelligence | AGI

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