Master Deep Learning for Computer Vision in TensorFlow[2025]

Use ConvNets & Vision Transformers to build projects in Image classification,generation,segmentation & Object detection

4.24 (215 reviews)

Udemy

platform

English

language

Data Science

What you will learn

The Basics of Tensors and Variables with Tensorflow

Mastery of the fundamentals of Machine Learning and The Machine Learning Developmment Lifecycle.

Basics of Tensorflow and training neural networks with TensorFlow 2.

Convolutional Neural Networks applied to Malaria Detection

Building more advanced Tensorflow models with Functional API, Model Subclassing and Custom Layers

Evaluating Classification Models using different metrics like: Precision,Recall,Accuracy and F1-score

Classification Model Evaluation with Confusion Matrix and ROC Curve

Tensorflow Callbacks, Learning Rate Scheduling and Model Check-pointing

Mitigating Overfitting and Underfitting with Dropout, Regularization, Data augmentation

Data augmentation with TensorFlow using TensorFlow image and Keras Layers

Advanced augmentation strategies like Cutmix and Mixup

Data augmentation with Albumentations with TensorFlow 2 and PyTorch

Custom Loss and Metrics in TensorFlow 2

Eager and Graph Modes in TensorFlow 2

Custom Training Loops in TensorFlow 2

Integrating Tensorboard with TensorFlow 2 for data logging, viewing model graphs, hyperparameter tuning and profiling

Machine Learning Operations (MLOps) with Weights and Biases

Experiment tracking with Wandb

Hyperparameter tuning with Wandb

Dataset versioning with Wandb

Model versioning with Wandb

Human emotions detection

Modern convolutional neural networks(Alexnet, Vggnet, Resnet, Mobilenet, EfficientNet)

Transfer learning

Visualizing convnet intermediate layers

Grad-cam method

Model ensembling and class imbalance

Transformers in Vision

Model deployment

Conversion from tensorflow to Onnx Model

Quantization Aware training

Building API with Fastapi

Deploying API to the Cloud

Object detection from scratch with YOLO

Image Segmentation from scratch with UNET model

People Counting from scratch with Csrnet

Digit generation with Variational autoencoders (VAE)

Face generation with Generative adversarial neural networks (GAN)

Course Gallery

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Master Deep Learning for Computer Vision in TensorFlow[2025]

What you will learn

The Basics of Tensors and Variables with Tensorflow

Mastery of the fundamentals of Machine Learning and The Machine Learning Developmment Lifecycle.

Basics of Tensorflow and training neural networks with TensorFlow 2.

Convolutional Neural Networks applied to Malaria Detection

Building more advanced Tensorflow models with Functional API, Model Subclassing and Custom Layers

Evaluating Classification Models using different metrics like: Precision,Recall,Accuracy and F1-score

Classification Model Evaluation with Confusion Matrix and ROC Curve

Tensorflow Callbacks, Learning Rate Scheduling and Model Check-pointing

Mitigating Overfitting and Underfitting with Dropout, Regularization, Data augmentation

Data augmentation with TensorFlow using TensorFlow image and Keras Layers

Advanced augmentation strategies like Cutmix and Mixup

Data augmentation with Albumentations with TensorFlow 2 and PyTorch

Custom Loss and Metrics in TensorFlow 2

Eager and Graph Modes in TensorFlow 2

Custom Training Loops in TensorFlow 2

Integrating Tensorboard with TensorFlow 2 for data logging, viewing model graphs, hyperparameter tuning and profiling

Machine Learning Operations (MLOps) with Weights and Biases

Experiment tracking with Wandb

Hyperparameter tuning with Wandb

Dataset versioning with Wandb

Model versioning with Wandb

Human emotions detection

Modern convolutional neural networks(Alexnet, Vggnet, Resnet, Mobilenet, EfficientNet)

Transfer learning

Visualizing convnet intermediate layers

Grad-cam method

Model ensembling and class imbalance

Transformers in Vision

Model deployment

Conversion from tensorflow to Onnx Model

Quantization Aware training

Building API with Fastapi

Deploying API to the Cloud

Object detection from scratch with YOLO

Image Segmentation from scratch with UNET model

People Counting from scratch with Csrnet

Digit generation with Variational autoencoders (VAE)

Face generation with Generative adversarial neural networks (GAN)

Course Gallery

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