Deep Learning for Computer Vision with Python and TensorFlow – Complete Course

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http://youtube.com/watch?v=IA3WxTTPXqQ

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/dri... • YouTube Channel: / @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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