Master Deep Learning for Computer Vision in TensorFlow[2024]

Master Deep Learning for Computer Vision in TensorFlow[2024], available at $49.99, has an average rating of 4.63, with 235 lectures, based on 147 reviews, and has 1399 subscribers.

You will learn about 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) This course is ideal for individuals who are Beginner Python Developers curious about Applying Deep Learning for Computer vision or Deep Learning for Computer vision Practitioners who want gain a mastery of how things work under the hood or Anyone who wants to master deep learning fundamentals and also practice deep learning for computer vision using best practices in TensorFlow. or Computer Vision practitioners who want to learn how state of art computer vision models are built and trained using deep learning. or Anyone wanting to deploy ML Models or Learners who want a practical approach to Deep learning for Computer vision or Computer Vision practitioners who want to learn how state of art computer vision models are built and trained using deep learning. It is particularly useful for Beginner Python Developers curious about Applying Deep Learning for Computer vision or Deep Learning for Computer vision Practitioners who want gain a mastery of how things work under the hood or Anyone who wants to master deep learning fundamentals and also practice deep learning for computer vision using best practices in TensorFlow. or Computer Vision practitioners who want to learn how state of art computer vision models are built and trained using deep learning. or Anyone wanting to deploy ML Models or Learners who want a practical approach to Deep learning for Computer vision or Computer Vision practitioners who want to learn how state of art computer vision models are built and trained using deep learning.

Enroll now: Master Deep Learning for Computer Vision in TensorFlow[2024]

Summary

Title: Master Deep Learning for Computer Vision in TensorFlow[2024]

Price: $49.99

Average Rating: 4.63

Number of Lectures: 235

Number of Published Lectures: 146

Number of Curriculum Items: 235

Number of Published Curriculum Objects: 146

Original Price: $89.99

Quality Status: approved

Status: Live

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)

Who Should Attend

• Beginner Python Developers curious about Applying Deep Learning for Computer vision
• Deep Learning for Computer vision Practitioners who want gain a mastery of how things work under the hood
• Anyone who wants to master deep learning fundamentals and also practice deep learning for computer vision using best practices in TensorFlow.
• Computer Vision practitioners who want to learn how state of art computer vision models are built and trained using deep learning.
• Anyone wanting to deploy ML Models
• Learners who want a practical approach to Deep learning for Computer vision
• Computer Vision practitioners who want to learn how state of art computer vision models are built and trained using deep learning.

Target Audiences

• Beginner Python Developers curious about Applying Deep Learning for Computer vision
• Deep Learning for Computer vision Practitioners who want gain a mastery of how things work under the hood
• Anyone who wants to master deep learning fundamentals and also practice deep learning for computer vision using best practices in TensorFlow.
• Computer Vision practitioners who want to learn how state of art computer vision models are built and trained using deep learning.
• Anyone wanting to deploy ML Models
• Learners who want a practical approach to Deep learning for Computer vision
• Computer Vision practitioners who want to learn how state of art computer vision models are built and trained using deep learning.