Deep Learning for Computer Vision with TensorFlow – Complete Course

Dive into a comprehensive 13-hour course on deep learning for computer vision using TensorFlow. Master the fundamentals of computer vision algorithms and their implementation in TensorFlow. Begin with tensor basics and progress to building neural networks for car price prediction and convolutional neural networks for malaria diagnosis. Explore advanced topics like model evaluation, performance improvement, data augmentation, and MLOps with Weights and Biases. Tackle human emotion detection, modern CNN architectures, transfer learning, and transformers in vision. Learn about model deployment, object detection with YOLO, and image generation using VAEs and GANs. Gain hands-on experience through practical projects and access accompanying code on Google Colab.

⌨️ Welcome
⌨️ Prerequisite
⌨️ What we shall Learn
⌨️ Basics
⌨️ Initialization and Casting
⌨️ Indexing
⌨️ Maths Operations
⌨️ Linear Algebra Operations
⌨️ Common TensorFlow Functions
⌨️ Ragged Tensors
⌨️ Sparse Tensors
⌨️ String Tensors
⌨️ Variables
⌨️ Task Understanding
⌨️ Data Preparation
⌨️ Linear Regression Model
⌨️ Error Sanctioning
⌨️ Training and Optimization
⌨️ Performance Measurement
⌨️ Validation and Testing
⌨️ Corrective Measures
⌨️ Task Understanding
⌨️ Data Preparation
⌨️ Data Visualization
⌨️ Data Processing
⌨️ How and Why ConvNets Work
⌨️ Building Convnets with TensorFlow
⌨️ Binary Crossentropy Loss
⌨️ Training Convnets
⌨️ Model Evaluation and Testing
⌨️ Loading and Saving Models to Google Drive
⌨️ Functional API
⌨️ Model Subclassing
⌨️ Custom Layers
⌨️ Precision, Recall and Accuracy
⌨️ Confusion Matrix
⌨️ ROC Plots
⌨️ TensorFlow Callbacks
⌨️ Learning Rate Scheduling
⌨️ Model Checkpointing
⌨️ Mitigating Overfitting and Underfitting
⌨️ Augmentation with tf.image and Keras Layers
⌨️ Mixup Augmentation
⌨️ Cutmix Augmentation
⌨️ Data Augmentation with Albumentations
⌨️ Custom Loss and Metrics
⌨️ Eager and Graph Modes
⌨️ Custom Training Loops
⌨️ Data Logging
⌨️ View Model Graphs
⌨️ Hyperparameter Tuning
⌨️ Profiling and Visualizations
⌨️ Experiment Tracking
⌨️ Hyperparameter Tuning
⌨️ Dataset Versioning
⌨️ Model Versioning
⌨️ Data Preparation
⌨️ Modeling and Training
⌨️ Data Augmentation
⌨️ TensorFlow Records
⌨️ AlexNet
⌨️ VGGNet
⌨️ ResNet
⌨️ Coding ResNet from Scratch
⌨️ MobileNet
⌨️ EfficientNet
⌨️ Feature Extraction
⌨️ Finetuning
⌨️ Visualizing Intermediate Layers
⌨️ Gradcam method
⌨️ Understanding ViTs
⌨️ Building ViTs from Scratch
⌨️ FineTuning Huggingface ViT
⌨️ Model Evaluation with Wandb
⌨️ Converting TensorFlow Model to Onnx format
⌨️ Understanding Quantization
⌨️ Practical Quantization of Onnx Model
⌨️ Quantization Aware Training
⌨️ Conversion to TensorFlow Lite
⌨️ How APIs work
⌨️ Building an API with FastAPI
⌨️ Deploying API to the Cloud
⌨️ Load Testing with Locust
⌨️ Introduction to Object Detection
⌨️ Understanding YOLO Algorithm
⌨️ Dataset Preparation
⌨️ YOLO Loss
⌨️ Data Augmentation
⌨️ Testing
⌨️ Introduction to Image Generation
⌨️ Understanding Variational Autoencoders
⌨️ VAE Training and Digit Generation
⌨️ Latent Space Visualization
⌨️ How GANs work
⌨️ The GAN Loss
⌨️ Improving GAN Training
⌨️ Face Generation with GANs
⌨️ What's Next