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

Deep Learning for Computer Vision with Python and TensorFlow – Complete Course
Learn the basics of computer vision with deep learning and how to implement the algorithms using Tensorflow.

Learn the basics of computer vision with deep learning and how to implement the algorithms using Tensorflow.

⭐️ 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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