Software Development

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



Study the fundamentals of pc imaginative and prescient with deep studying and methods to implement the algorithms utilizing Tensorflow.

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Writer: Folefac Martins from Neuralearn.ai
Extra Courses: www.neuralearn.ai
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⭐️ Contents ⭐️

Introduction
⌨️ (0:00:00) Welcome
⌨️ (0:05:54) Prerequisite
⌨️ (0:06:11) What we will Study

Tensors and Variables
⌨️ (0:12:12) Fundamentals
⌨️ (0:19:26) Initialization and Casting
⌨️ (1:07:31) Indexing
⌨️ (1:16:15) Maths Operations
⌨️ (1:55:02) Linear Algebra Operations
⌨️ (2:56:21) Frequent TensorFlow Capabilities
⌨️ (3:50:15) Ragged Tensors
⌨️ (4:01:41) Sparse Tensors
⌨️ (4:04:23) String Tensors
⌨️ (4:07:45) Variables

Constructing Neural Networks with TensorFlow [Car Price Prediction]
⌨️ (4:14:52) Process Understanding
⌨️ (4:19:47) Knowledge Preparation
⌨️ (4:54:47) Linear Regression Mannequin
⌨️ (5:10:18) Error Sanctioning
⌨️ (5:24:53) Coaching and Optimization
⌨️ (5:41:22) Efficiency Measurement
⌨️ (5:44:18) Validation and Testing
⌨️ (6:04:30) Corrective Measures

Constructing Convolutional Neural Networks with TensorFlow [Malaria Diagnosis]
⌨️ (6:28:50) Process Understanding
⌨️ (6:37:40) Knowledge Preparation
⌨️ (6:57:40) Knowledge Visualization
⌨️ (7:00:20) Knowledge Processing
⌨️ (7:08:50) How and Why ConvNets Work
⌨️ (7:56:15) Constructing Convnets with TensorFlow
⌨️ (8:02:39) Binary Crossentropy Loss
⌨️ (8:10:15) Coaching Convnets
⌨️ (8:23:33) Mannequin Analysis and Testing
⌨️ (8:29:15) Loading and Saving Fashions to Google Drive

Constructing Extra Superior Fashions in Teno Convolutional Neural Networks with TensorFlow [Malaria Diagnosis]
⌨️ (8:47:10) Practical API
⌨️ (9:03:48) Mannequin Subclassing
⌨️ (9:19:05) Customized Layers

Evaluating Classification Fashions [Malaria Diagnosis]
⌨️ (9:36:45) Precision, Recall and Accuracy
⌨️ (10:00:35) Confusion Matrix
⌨️ (10:10:10) ROC Plots

Enhancing Mannequin Efficiency [Malaria Diagnosis]
⌨️ (10:18:10) TensorFlow Callbacks
⌨️ (10:43:55) Learning Charge Scheduling
⌨️ (11:01:25) Mannequin Checkpointing
⌨️ (11:09:25) Mitigating Overfitting and Underfitting

Knowledge Augmentation [Malaria Diagnosis]
⌨️ (11:38:50) Augmentation with tf.picture and Keras Layers
⌨️ (12:38:00) Mixup Augmentation
⌨️ (12:56:35) Cutmix Augmentation
⌨️ (13:38:30) Knowledge Augmentation with Albumentations

Superior TensorFlow Subjects [Malaria Diagnosis]
⌨️ (13:58:35) Customized Loss and Metrics
⌨️ (14:18:30) Keen and Graph Modes
⌨️ (14:31:23) Customized Coaching Loops

Tensorboard Integration [Malaria Diagnosis]
⌨️ (14:57:00) Knowledge Logging
⌨️ (15:29:00) View Mannequin Graphs
⌨️ (15:31:45) Hyperparameter Tuning
⌨️ (15:52:40) Profiling and Visualizations

MLOps with Weights and Biases [Malaria Diagnosis]
⌨️ (16:00:35) Experiment Monitoring
⌨️ (16:55:02) Hyperparameter Tuning
⌨️ (17:17:15) Dataset Versioning
⌨️ (18:00:23) Mannequin Versioning

Human Feelings Detection
⌨️ (18:16:55) Knowledge Preparation
⌨️ (18:45:38) Modeling and Coaching
⌨️ (19:36:42) Knowledge Augmentation
⌨️ (19:54:30) TensorFlow Information

Trendy 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

Switch Learning [Human Emotions Detection]
⌨️ (22:38:15) Function Extraction
⌨️ (23:02:25) Finetuning

Understanding the Blackbox [Human Emotions Detection]
⌨️ (23:15:33) Visualizing Intermediate Layers
⌨️ (23:36:20) Gradcam methodology

Transformers in Vision [Human Emotions Detection]
⌨️ (23:57:35) Understanding ViTs
⌨️ (24:51:17) Constructing ViTs from Scratch
⌨️ (25:42:39) FineTuning Huggingface ViT
⌨️ (26:05:52) Mannequin Analysis with Wandb

Mannequin Deployment [Human Emotions Detection]
⌨️ (26:27:13) Changing TensorFlow Mannequin to Onnx format
⌨️ (26:52:26) Understanding Quantization
⌨️ (27:13:08) Sensible Quantization of Onnx Mannequin
⌨️ (27:22:01) Quantization Conscious Coaching
⌨️ (27:39:55) Conversion to TensorFlow Lite
⌨️ (27:58:28) How APIs work
⌨️ (28:18:28) Constructing 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) Knowledge Augmentation
⌨️ (33:27:33) Testing

Picture Era
⌨️ (33:59:28) Introduction to Picture Era
⌨️ (34:03:18) Understanding Variational Autoencoders
⌨️ (34:20:46) VAE Coaching and Digit Era
⌨️ (35:06:05) Latent House Visualization
⌨️ (35:21:36) How GANs work
⌨️ (35:43:30) The GAN Loss
⌨️ (36:01:38) Enhancing GAN Coaching
⌨️ (36:25:02) Face Era with GANs

Conclusion
⌨️ (37:15:45) What’s Subsequent

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