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⌨️ How and Why ConvNets Work
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Classroom Contents
Deep Learning for Computer Vision with TensorFlow – Complete Course
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- 1 ⌨️ Welcome
- 2 ⌨️ Prerequisite
- 3 ⌨️ What we shall Learn
- 4 ⌨️ Basics
- 5 ⌨️ Initialization and Casting
- 6 ⌨️ Indexing
- 7 ⌨️ Maths Operations
- 8 ⌨️ Linear Algebra Operations
- 9 ⌨️ Common TensorFlow Functions
- 10 ⌨️ Ragged Tensors
- 11 ⌨️ Sparse Tensors
- 12 ⌨️ String Tensors
- 13 ⌨️ Variables
- 14 ⌨️ Task Understanding
- 15 ⌨️ Data Preparation
- 16 ⌨️ Linear Regression Model
- 17 ⌨️ Error Sanctioning
- 18 ⌨️ Training and Optimization
- 19 ⌨️ Performance Measurement
- 20 ⌨️ Validation and Testing
- 21 ⌨️ Corrective Measures
- 22 ⌨️ Task Understanding
- 23 ⌨️ Data Preparation
- 24 ⌨️ Data Visualization
- 25 ⌨️ Data Processing
- 26 ⌨️ How and Why ConvNets Work
- 27 ⌨️ Building Convnets with TensorFlow
- 28 ⌨️ Binary Crossentropy Loss
- 29 ⌨️ Training Convnets
- 30 ⌨️ Model Evaluation and Testing
- 31 ⌨️ Loading and Saving Models to Google Drive
- 32 ⌨️ Functional API
- 33 ⌨️ Model Subclassing
- 34 ⌨️ Custom Layers
- 35 ⌨️ Precision, Recall and Accuracy
- 36 ⌨️ Confusion Matrix
- 37 ⌨️ ROC Plots
- 38 ⌨️ TensorFlow Callbacks
- 39 ⌨️ Learning Rate Scheduling
- 40 ⌨️ Model Checkpointing
- 41 ⌨️ Mitigating Overfitting and Underfitting
- 42 ⌨️ Augmentation with tf.image and Keras Layers
- 43 ⌨️ Mixup Augmentation
- 44 ⌨️ Cutmix Augmentation
- 45 ⌨️ Data Augmentation with Albumentations
- 46 ⌨️ Custom Loss and Metrics
- 47 ⌨️ Eager and Graph Modes
- 48 ⌨️ Custom Training Loops
- 49 ⌨️ Data Logging
- 50 ⌨️ View Model Graphs
- 51 ⌨️ Hyperparameter Tuning
- 52 ⌨️ Profiling and Visualizations
- 53 ⌨️ Experiment Tracking
- 54 ⌨️ Hyperparameter Tuning
- 55 ⌨️ Dataset Versioning
- 56 ⌨️ Model Versioning
- 57 ⌨️ Data Preparation
- 58 ⌨️ Modeling and Training
- 59 ⌨️ Data Augmentation
- 60 ⌨️ TensorFlow Records
- 61 ⌨️ AlexNet
- 62 ⌨️ VGGNet
- 63 ⌨️ ResNet
- 64 ⌨️ Coding ResNet from Scratch
- 65 ⌨️ MobileNet
- 66 ⌨️ EfficientNet
- 67 ⌨️ Feature Extraction
- 68 ⌨️ Finetuning
- 69 ⌨️ Visualizing Intermediate Layers
- 70 ⌨️ Gradcam method
- 71 ⌨️ Understanding ViTs
- 72 ⌨️ Building ViTs from Scratch
- 73 ⌨️ FineTuning Huggingface ViT
- 74 ⌨️ Model Evaluation with Wandb
- 75 ⌨️ Converting TensorFlow Model to Onnx format
- 76 ⌨️ Understanding Quantization
- 77 ⌨️ Practical Quantization of Onnx Model
- 78 ⌨️ Quantization Aware Training
- 79 ⌨️ Conversion to TensorFlow Lite
- 80 ⌨️ How APIs work
- 81 ⌨️ Building an API with FastAPI
- 82 ⌨️ Deploying API to the Cloud
- 83 ⌨️ Load Testing with Locust
- 84 ⌨️ Introduction to Object Detection
- 85 ⌨️ Understanding YOLO Algorithm
- 86 ⌨️ Dataset Preparation
- 87 ⌨️ YOLO Loss
- 88 ⌨️ Data Augmentation
- 89 ⌨️ Testing
- 90 ⌨️ Introduction to Image Generation
- 91 ⌨️ Understanding Variational Autoencoders
- 92 ⌨️ VAE Training and Digit Generation
- 93 ⌨️ Latent Space Visualization
- 94 ⌨️ How GANs work
- 95 ⌨️ The GAN Loss
- 96 ⌨️ Improving GAN Training
- 97 ⌨️ Face Generation with GANs
- 98 ⌨️ What's Next
