Gain insights into image data processing and CNNs for image classification using TensorFlow. Delve into CNN architectures and their applications, requiring Python and TensorFlow knowledge.

irml.tar.gz

image-rec

If you want to dive into the technology behind computer vision and self driving cars, this is where to start.

In this course you'll learn how to process data from image files and create convolutional neural networks (CNNs) to classify different types of images. After completing this course, you will have a solid understanding of why CNNs work and which CNN architectures to use for different tasks.

The code for this course is built around the TensorFlow framework, one of the premier frameworks for industry machine learning, and the Python pandas library for data analysis. Knowledge of Python and TensorFlow are prerequisites. 

This course was created by AdaptiLab, a company specializing in evaluating, sourcing, and upskilling enterprise machine learning talent. It is built in collaboration with industry machine learning experts from Google, Microsoft, Amazon, and Apple.

Image Recognition with Machine Learning

# Chapter Goals:
- Learn how to apply a sparse softmax cross entropy


# A. Sparse representation

As mentioned in the Initialization chapter, the CIFAR-10 labels are sparsely represented. This means that, rather than being one-hot vectors, each label is just the index of its corresponding image class. Most datasets will use sparse representation for their labels, since it saves a ton of space compared to one-hot representation (especially if there are many image classes).

For training the model, we use a sparse version of softmax cross entropy. In TensorFlow, this is provided through the [tf.nn.sparse_softmax_cross_entropy_with_logits](https://www.tensorflow.org/api_docs/python/tf/nn/sparse_softmax_cross_entropy_with_logits) function.

Below we show the full code for setting up and training the model:


Understand sparse representation of labels.

What you'll learn from this course

Image Processing

CNN

SqueezeNet

ResNet

Sparse Labels

Chapter Goals:

A. Sparse