LEARNING PATH: TensorFlow: Computer Vision with TensorFlow
4.1 (27 ratings)
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LEARNING PATH: TensorFlow: Computer Vision with TensorFlow

Learn image processing and neural networks with Tensorflow from scratch
4.1 (27 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
331 students enrolled
Created by Packt Publishing
Last updated 3/2018
English
English [Auto-generated]
Current price: $139.99 Original price: $199.99 Discount: 30% off
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This course includes
  • 4 hours on-demand video
  • 1 downloadable resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll 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
Course content
Expand all 32 lectures 04:00:05
+ Learning Computer Vision with TensorFlow
15 lectures 02:01:20

This video provides an overview of the entire course.

Preview 02:43

In this video, we are going to install package dependency manger with Miniconda, TensorFlow, and its dependencies. We will then launch the Jupyter Notebook.

Setting Up TensorFlow Environment
03:17

In this video, we are going to learn about the Loss function in context of deep learning.

TensorFlow- Keras Loss Functions
11:15

In this video, we are going to master in evaluation metrics and implement them in TensorFlow-Keras.

TensorFlow-Keras Evaluation Metrics
10:34

In this video, we are going to look at optimizers in deep learning.

TensorFlow-Keras Optimizers
09:39

In this video, we are going to learn in-depth about the CNNs.

Preview 11:38

In this video, you will understand more about TensorFlowKeras layer.

TensorFlow- Keras Layers
08:42

TensorFlow-Keras Functional API

TensorFlow-Keras Functional API
12:36

In this video, we are going to look at the how to create Image Preprocessing methods and augmentation techniques for deep learning models.

Image Preprocessing and Augmentation
10:12

In this video, we will explore the cat and dog dataset.

Cat and Dog Dataset
03:40

In this video, we will discuss the VGG network architecture.

VGG Network Architecture
06:31

In this video, we will go over the implementation of the VGG architecture.

VGG Implementation in TensorFlow-Keras
06:18

In this video, we will train and evaluate the CIFAR-10 dataset.

Model Training and Evaluation
04:06

In this video, we will learn about feature extraction.

Transfer Learning – Feature Extraction
10:32

In this video, we will cover another method of transfer learning called fine tuning.

Transfer Learning - Fine Tuning
09:37
Test Your Knowledge
5 questions
+ Advanced Computer Vision with TensorFlow
17 lectures 01:58:45

This video provides an overview of the entire course.

Preview 01:35

In this video, we are going to learn about the CIFAR10 dataset.

Loading and Exploring CIFAR10 Dataset
04:06

In this video, we are going to learn about the architecture design of the SqueezeNet model.

SqueezeNet Architecture Design
08:09

In this video, we are going implement the SqueezeNet model in the TensorFlowKeras API.

SqueezeNet Implementation
09:21

In this video, we are going to train and evaluate the SqueezeNet model on the CIFAR10 dataset.

Training and Evaluating SqueezeNet
08:09

In this video, we will learn about loading and exploring the flower dataset.

Loading and Exploring Flower Dataset
05:38

In this video, we will learn about ResNet architecture.

ResNet Architecture Design
09:41

In this video, we are going learn about TensorFlow-Keras implementation of residual learning network.

ResNet Implementation
07:18

In this video, we are going to train and evaluate our ResNet model on our flower dataset.

Training and Evaluating ResNet
06:39

In this video, we will learn about loading and exploring the ImageNet dataset.

Loading and Exploring ImageNet Dataset
08:27

In this video, we are going to learn about the architecture design of the Xception model.

Xception Architecture Design
07:56

In this video, we are going to implement the Xception model using the TensorFlow-Keras API.

Xception Implementation
08:40

In this video, we are going to train and evaluate our Xception model on our ImageNet dataset.

Training and Evaluating Xception
07:14

In this video, we will learn about loading and exploring the MNIST dataset.

Loading and Exploring MNIST Dataset
02:34

In this video, we are going to learn about the ACGAN architecture design.

ACGAN Architecture Design
04:33

In this video, we are going to implement the ACGAN using the TensorFlow-Keras API.

ACGAN Implementation
08:37

In this video, we are going to train and evaluate our ACGAN model.

Training and Evaluating ACGAN
10:08
Test Your Knowledge
4 questions
Requirements
  • Basic knowledge of TensorFlow will help you understand concepts more effectively
  • Prior working knowledge on Python is assumed
Description

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.

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