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

Video Description

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