LEARNING PATH- TensorFlow- Computer Vision with TensorFlow

LEARNING PATH: TensorFlow: Computer Vision with TensorFlow, available at $19.99, has an average rating of 3.55, with 32 lectures, 2 quizzes, based on 34 reviews, and has 381 subscribers.

You will learn about Learn to build powerful multiclass image classifiers Understand how to build a neural feature extractor that can embed images into a dense and rich vector space Perform fine-tuning optimization on new predictive tasks using pre-trained neural networks Build functional model class and methods with Keras Know how to choose the right loss function and evaluation metric for the right task Build a computational graph representation of a neural network Train a neural network with automatic back propagation Learn to optimize a neural network with stochastic gradient descent and other advanced optimization methods This course is ideal for individuals who are This Learning Path is for Python developers who are interested in learning how develop applications and perform image processing using TensorFlow. It is particularly useful for This Learning Path is for Python developers who are interested in learning how develop applications and perform image processing using TensorFlow.

Enroll now: LEARNING PATH: TensorFlow: Computer Vision with TensorFlow

Summary

Title: LEARNING PATH: TensorFlow: Computer Vision with TensorFlow

Price: $19.99

Average Rating: 3.55

Number of Lectures: 32

Number of Quizzes: 2

Number of Published Lectures: 32

Number of Published Quizzes: 2

Number of Curriculum Items: 34

Number of Published Curriculum Objects: 34

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Learn to build powerful multiclass image classifiers

Understand how to build a neural feature extractor that can embed images into a dense and rich vector space

Perform fine-tuning optimization on new predictive tasks using pre-trained neural networks

Build functional model class and methods with Keras

Know how to choose the right loss function and evaluation metric for the right task

Build a computational graph representation of a neural network

Train a neural network with automatic back propagation

Learn to optimize a neural network with stochastic gradient descent and other advanced optimization methods

Who Should Attend

This Learning Path is for Python developers who are interested in learning how develop applications and perform image processing using TensorFlow.

Target Audiences

This Learning Path is for Python developers who are interested in learning how develop applications and perform image processing using TensorFlow.

TensorFlow has been gaining immense popularity over the past few months, due to its power and simplicity to use. So, if you’re a Python developer who is interested in learning how to create applications and perform image processing using TensorFlow, then you should surely go for this Learning Path.

Packt’s Video Learning Path is a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.

The highlights of this Learning Path are:

Learn how to create image processing applications using free tools and libraries

Perform advanced image processing with TensorFlowAPIs

Understand and optimize various features of TensorFlow by building deep learning state-of-the-art models

Let’s take a quick look at your learning journey. This Learning Path starts off with an introduction to image processing. You will then walk through graph tensor which is used for image classification. Starting with the basic 2D images, you will gradually be taken through more complex images, colors, shapes, and so on. You will also learn to make use of Python API to classify and train your model to identify objects in an image.

Next, you will learn about convolutional neural networks (CNNs), its architecture, and why they perform well in the image take. You will then dive into the different layers available in TensorFlow.? You will also learn to construct the neural network feature extractor to embed images into a dense and rich vector space.

Moving ahead, you will learn to construct efficient CNN architectures with CNN Squeeze layers and delayed downsampling. You will learn about residual learning with skip connections and deep residual blocks, and see how to implement a deep residual neural network for image recognition. Next, you will find out about Google’s Inception module and depth-wise separable convolutions and understand how to construct an extreme Inception architecture with TF-Keras. Finally, you will be introduced to the exciting new world of adversarial neural networks, which are responsible for recent breakthroughs in synthetic image generation and implement an auxiliary conditional generative adversarial networks (GAN).

By the end of this Learning Path, you will be able to create applications and perform image processing efficiently.

Meet Your Expert:

We have the best work of the following esteemed author to ensure that your learning journey is smooth:

Marvin Bertin has authored online deep learning courses. He is the technical editor of a deep learning book and a conference speaker. He has a bachelor’s degree in mechanical engineering and master’s in data science. He has? worked at a deep learning startup developing neural network architectures. He is currently working in the biotech industry building NLP machine learning solutions. At the forefront of next generation DNA sequencing, he builds intelligent applications with machine learning and deep learning for precision medicine.

Course Curriculum

Chapter 1: Learning Computer Vision with TensorFlow

Lecture 1: The Course Overview

Lecture 2: Setting Up TensorFlow Environment

Lecture 3: TensorFlow- Keras Loss Functions

Lecture 4: TensorFlow-Keras Evaluation Metrics

Lecture 5: TensorFlow-Keras Optimizers

Lecture 6: What are CNNs?

Lecture 7: TensorFlow- Keras Layers

Lecture 8: TensorFlow-Keras Functional API

Lecture 9: Image Preprocessing and Augmentation

Lecture 10: Cat and Dog Dataset

Lecture 11: VGG Network Architecture

Lecture 12: VGG Implementation in TensorFlow-Keras

Lecture 13: Model Training and Evaluation

Lecture 14: Transfer Learning – Feature Extraction

Lecture 15: Transfer Learning – Fine Tuning

Chapter 2: Advanced Computer Vision with TensorFlow

Lecture 1: The Course Overview

Lecture 2: Loading and Exploring CIFAR10 Dataset

Lecture 3: SqueezeNet Architecture Design

Lecture 4: SqueezeNet Implementation

Lecture 5: Training and Evaluating SqueezeNet

Lecture 6: Loading and Exploring Flower Dataset

Lecture 7: ResNet Architecture Design

Lecture 8: ResNet Implementation

Lecture 9: Training and Evaluating ResNet

Lecture 10: Loading and Exploring ImageNet Dataset

Lecture 11: Xception Architecture Design

Lecture 12: Xception Implementation

Lecture 13: Training and Evaluating Xception

Lecture 14: Loading and Exploring MNIST Dataset

Lecture 15: ACGAN Architecture Design

Lecture 16: ACGAN Implementation

Lecture 17: Training and Evaluating ACGAN

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 3 votes

2 stars: 2 votes

3 stars: 6 votes

4 stars: 8 votes

5 stars: 15 votes

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