Advanced Computer Vision with TensorFlow

Watch Advanced Computer Vision with TensorFlow

  • 2017
  • 1 Season

Advanced Computer Vision with TensorFlow is an online course offered by Packt Publishing that teaches users how to build advanced deep learning models using TensorFlow. The course is structured in a way that is suitable for both beginners and experienced users alike.

The course starts by introducing users to the basics of deep learning and computer vision. Users will learn about convolutional neural networks (CNNs), which is a type of neural network that is particularly good at image recognition tasks. They will also learn about transfer learning, which is a technique that allows users to use pre-trained models to speed up the training process.

The course then moves on to more advanced topics, such as object detection and image segmentation. Users will learn how to use TensorFlow to build models that can detect and classify objects in images. They will also learn how to segment images into different regions, which is useful for tasks such as medical imaging and self-driving cars.

Throughout the course, users will be provided with practical examples and exercises to reinforce their learning. They will also be introduced to a number of useful tools and frameworks, such as the TensorFlow Object Detection API.

One of the great features of Advanced Computer Vision with TensorFlow is that it is presented in a very clear and concise manner. The course is divided into small, bite-sized chunks that are easy to follow. Each lesson is accompanied by a video tutorial, as well as a written summary and code examples.

Users will also have access to a number of resources to help them in their learning. This includes a GitHub repository containing all of the code examples used in the course, as well as a forum where users can ask questions and get help from other members of the community.

Overall, I would highly recommend Advanced Computer Vision with TensorFlow to anyone who is interested in deep learning and computer vision. The course is well-structured and provides users with a solid foundation in these topics, while also covering more advanced topics such as object detection and image segmentation. If you are looking to take your skills in this area to the next level, then this course is definitely worth checking out.

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Seasons
Training and Evaluating ACGAN
17. Training and Evaluating ACGAN
November 27, 2017
In this video, we are going to train and evaluate our ACGAN model.
Training and Evaluating ACGAN
17. Training and Evaluating ACGAN
November 27, 2017
In this video, we are going to train and evaluate our ACGAN model.
ACGAN Implementation
16. ACGAN Implementation
November 27, 2017
In this video, we are going to implement the ACGAN using the TensorFlow-Keras API.
ACGAN Implementation
16. ACGAN Implementation
November 27, 2017
In this video, we are going to implement the ACGAN using the TensorFlow-Keras API.
ACGAN Architecture Design
15. ACGAN Architecture Design
November 27, 2017
In this video, we are going to learn about the ACGAN architecture design.
ACGAN Architecture Design
15. ACGAN Architecture Design
November 27, 2017
In this video, we are going to learn about the ACGAN architecture design.
Loading and Exploring MNIST Dataset
14. Loading and Exploring MNIST Dataset
November 27, 2017
In this video, we will learn about loading and exploring the MNIST dataset.
Loading and Exploring MNIST Dataset
14. Loading and Exploring MNIST Dataset
November 27, 2017
In this video, we will learn about loading and exploring the MNIST dataset.
Training and Evaluating Xception
13. Training and Evaluating Xception
November 27, 2017
In this video, we are going to train and evaluate our Xception model on our ImageNet dataset.
Training and Evaluating Xception
13. Training and Evaluating Xception
November 27, 2017
In this video, we are going to train and evaluate our Xception model on our ImageNet dataset.
Xception Implementation
12. Xception Implementation
November 27, 2017
In this video, we are going to implement the Xception model using the TensorFlow-Keras API.
Xception Implementation
12. Xception Implementation
November 27, 2017
In this video, we are going to implement the Xception model using the TensorFlow-Keras API.
Xception Architecture Design
11. Xception Architecture Design
November 27, 2017
In this video, we are going to learn about the architecture design of the Xception model.
Xception Architecture Design
11. Xception Architecture Design
November 27, 2017
In this video, we are going to learn about the architecture design of the Xception model.
Loading and Exploring ImageNet Dataset
10. Loading and Exploring ImageNet Dataset
November 27, 2017
In this video, we will learn about loading and exploring the ImageNet dataset.
Loading and Exploring ImageNet Dataset
10. Loading and Exploring ImageNet Dataset
November 27, 2017
In this video, we will learn about loading and exploring the ImageNet dataset.
Training and Evaluating ResNet
9. Training and Evaluating ResNet
November 27, 2017
In this video, we are going to train and evaluate our ResNet model on our flower dataset.
Training and Evaluating ResNet
9. Training and Evaluating ResNet
November 27, 2017
In this video, we are going to train and evaluate our ResNet model on our flower dataset.
ResNet Implementation
8. ResNet Implementation
November 27, 2017
In this video, we are going learn about TensorFlow-Keras implementation of residual learning network.
ResNet Implementation
8. ResNet Implementation
November 27, 2017
In this video, we are going learn about TensorFlow-Keras implementation of residual learning network.
ResNet Architecture Design
7. ResNet Architecture Design
November 27, 2017
In this video, we will learn about ResNet architecture.
ResNet Architecture Design
7. ResNet Architecture Design
November 27, 2017
In this video, we will learn about ResNet architecture.
Loading and Exploring Flower Dataset
6. Loading and Exploring Flower Dataset
November 27, 2017
In this video, we will learn about loading and exploring the flower dataset.
Loading and Exploring Flower Dataset
6. Loading and Exploring Flower Dataset
November 27, 2017
In this video, we will learn about loading and exploring the flower dataset.
Training and Evaluating SqueezeNet
5. Training and Evaluating SqueezeNet
November 27, 2017
In this video, we are going to train and evaluate the SqueezeNet model on the CIFAR10 dataset.
Training and Evaluating SqueezeNet
5. Training and Evaluating SqueezeNet
November 27, 2017
In this video, we are going to train and evaluate the SqueezeNet model on the CIFAR10 dataset.
SqueezeNet Implementation
4. SqueezeNet Implementation
November 27, 2017
In this video, we are going implement the SqueezeNet model in the TensorFlowKeras API.
SqueezeNet Implementation
4. SqueezeNet Implementation
November 27, 2017
In this video, we are going implement the SqueezeNet model in the TensorFlowKeras API.
SqueezeNet Architecture Design
3. SqueezeNet Architecture Design
November 27, 2017
In this video, we are going to learn about the architecture design of the SqueezeNet model.
SqueezeNet Architecture Design
3. SqueezeNet Architecture Design
November 27, 2017
In this video, we are going to learn about the architecture design of the SqueezeNet model.
Loading and Exploring CIFAR10 Dataset
2. Loading and Exploring CIFAR10 Dataset
November 27, 2017
In this video, we are going to learn about the CIFAR10 dataset.
Loading and Exploring CIFAR10 Dataset
2. Loading and Exploring CIFAR10 Dataset
November 27, 2017
In this video, we are going to learn about the CIFAR10 dataset.
The Course Overview
1. The Course Overview
November 27, 2017
This video provides an overview of the entire course.
The Course Overview
1. The Course Overview
November 27, 2017
This video provides an overview of the entire course.
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  • Premiere Date
    November 27, 2017
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