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Explore Deep Learning for Computer Vision with Python and TensorFlow – Complete Course with 1,219,099 views, 31,364 likes, and 819 comments. Experience the impact of this video content that has captured audience attention.

Learn the basics of computer vision with deep learning and how to implement the algorithms using Tensorflow. Author: Folefac Martins from Neuralearn.ai More Courses: www.neuralearn.ai Link to Code: https://colab.research.google.com/drive/18u1KDx-9683iZNPxSDZ6dOv9319ZuEC_ YouTube Channel: https://www.youtube.com/@neuralearn ❤️ Try interactive Python courses we love, right in your browser: https://scrimba.com/freeCodeCamp-Python (Made possible by a grant from our friends at Scrimba) ⭐️ Contents ⭐️ Introduction ⌨️ (0:00:00) Welcome ⌨️ (0:05:54) Prerequisite ⌨️ (0:06:11) What we shall Learn Tensors and Variables ⌨️ (0:12:12) Basics ⌨️ (0:19:26) Initialization and Casting ⌨️ (1:07:31) Indexing ⌨️ (1:16:15) Maths Operations ⌨️ (1:55:02) Linear Algebra Operations ⌨️ (2:56:21) Common TensorFlow Functions ⌨️ (3:50:15) Ragged Tensors ⌨️ (4:01:41) Sparse Tensors ⌨️ (4:04:23) String Tensors ⌨️ (4:07:45) Variables Building Neural Networks with TensorFlow [Car Price Prediction] ⌨️ (4:14:52) Task Understanding ⌨️ (4:19:47) Data Preparation ⌨️ (4:54:47) Linear Regression Model ⌨️ (5:10:18) Error Sanctioning ⌨️ (5:24:53) Training and Optimization ⌨️ (5:41:22) Performance Measurement ⌨️ (5:44:18) Validation and Testing ⌨️ (6:04:30) Corrective Measures Building Convolutional Neural Networks with TensorFlow [Malaria Diagnosis] ⌨️ (6:28:50) Task Understanding ⌨️ (6:37:40) Data Preparation ⌨️ (6:57:40) Data Visualization ⌨️ (7:00:20) Data Processing ⌨️ (7:08:50) How and Why ConvNets Work ⌨️ (7:56:15) Building Convnets with TensorFlow ⌨️ (8:02:39) Binary Crossentropy Loss ⌨️ (8:10:15) Training Convnets ⌨️ (8:23:33) Model Evaluation and Testing ⌨️ (8:29:15) Loading and Saving Models to Google Drive Building More Advanced Models in Teno Convolutional Neural Networks with TensorFlow [Malaria Diagnosis] ⌨️ (8:47:10) Functional API ⌨️ (9:03:48) Model Subclassing ⌨️ (9:19:05) Custom Layers Evaluating Classification Models [Malaria Diagnosis] ⌨️ (9:36:45) Precision, Recall and Accuracy ⌨️ (10:00:35) Confusion Matrix ⌨️ (10:10:10) ROC Plots Improving Model Performance [Malaria Diagnosis] ⌨️ (10:18:10) TensorFlow Callbacks ⌨️ (10:43:55) Learning Rate Scheduling ⌨️ (11:01:25) Model Checkpointing ⌨️ (11:09:25) Mitigating Overfitting and Underfitting Data Augmentation [Malaria Diagnosis] ⌨️ (11:38:50) Augmentation with tf.image and Keras Layers ⌨️ (12:38:00) Mixup Augmentation ⌨️ (12:56:35) Cutmix Augmentation ⌨️ (13:38:30) Data Augmentation with Albumentations Advanced TensorFlow Topics [Malaria Diagnosis] ⌨️ (13:58:35) Custom Loss and Metrics ⌨️ (14:18:30) Eager and Graph Modes ⌨️ (14:31:23) Custom Training Loops Tensorboard Integration [Malaria Diagnosis] ⌨️ (14:57:00) Data Logging ⌨️ (15:29:00) View Model Graphs ⌨️ (15:31:45) Hyperparameter Tuning ⌨️ (15:52:40) Profiling and Visualizations MLOps with Weights and Biases [Malaria Diagnosis] ⌨️ (16:00:35) Experiment Tracking ⌨️ (16:55:02) Hyperparameter Tuning ⌨️ (17:17:15) Dataset Versioning ⌨️ (18:00:23) Model Versioning Human Emotions Detection ⌨️ (18:16:55) Data Preparation ⌨️ (18:45:38) Modeling and Training ⌨️ (19:36:42) Data Augmentation ⌨️ (19:54:30) TensorFlow Records Modern Convolutional Neural Networks [Human Emotions Detection] ⌨️ (20:31:25) AlexNet ⌨️ (20:48:35) VGGNet ⌨️ (20:59:50) ResNet ⌨️ (21:34:07) Coding ResNet from Scratch ⌨️ (21:56:17) MobileNet ⌨️ (22:20:43) EfficientNet Transfer Learning [Human Emotions Detection] ⌨️ (22:38:15) Feature Extraction ⌨️ (23:02:25) Finetuning Understanding the Blackbox [Human Emotions Detection] ⌨️ (23:15:33) Visualizing Intermediate Layers ⌨️ (23:36:20) Gradcam method Transformers in Vision [Human Emotions Detection] ⌨️ (23:57:35) Understanding ViTs ⌨️ (24:51:17) Building ViTs from Scratch ⌨️ (25:42:39) FineTuning Huggingface ViT ⌨️ (26:05:52) Model Evaluation with Wandb Model Deployment [Human Emotions Detection] ⌨️ (26:27:13) Converting TensorFlow Model to Onnx format ⌨️ (26:52:26) Understanding Quantization ⌨️ (27:13:08) Practical Quantization of Onnx Model ⌨️ (27:22:01) Quantization Aware Training ⌨️ (27:39:55) Conversion to TensorFlow Lite ⌨️ (27:58:28) How APIs work ⌨️ (28:18:28) Building an API with FastAPI ⌨️ (29:39:10) Deploying API to the Cloud ⌨️ (29:51:35) Load Testing with Locust Object Detection with YOLO ⌨️ (30:05:29) Introduction to Object Detection ⌨️ (30:11:39) Understanding YOLO Algorithm ⌨️ (31:15:17) Dataset Preparation ⌨️ (31:58:27) YOLO Loss ⌨️ (33:02:58) Data Augmentation ⌨️ (33:27:33) Testing Image Generation ⌨️ (33:59:28) Introduction to Image Generation ⌨️ (34:03:18) Understanding Variational Autoencoders ⌨️ (34:20:46) VAE Training and Digit Generation ⌨️ (35:06:05) Latent Space Visualization ⌨️ (35:21:36) How GANs work ⌨️ (35:43:30) The GAN Loss ⌨️ (36:01:38) Improving GAN Training ⌨️ (36:25:02) Face Generation with GANs Conclusion ⌨️ (37:15:45) What's Next

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