### TensorFlow: Deep Neural Networks and Image Classification

**Overview/Description**
**Expected Duration**
**Lesson Objectives**
**Course Number**
**Expertise Level**

**Overview/Description**
Discover how to apply deep learning techniques to images, and how to leverage TensorFlow estimators in building image classification models.

**Expected Duration (hours)**
1.3

**Lesson Objectives****TensorFlow: Deep Neural Networks and Image Classification**

distinguish between traditional machine learning and deep learning
recognize the architecture and design of a neural network
identify what is meant by model weights or model parameters
identify the precise operations performed by a neuron
recognize gradient descent as the training process in a neural network
distinguish between the operations in the forward and backward passes during training
describe how images are fed into a machine learning algorithm
configure TensorFlow and use Jupyter notebooks
load and explore the MNIST dataset for image classification
train a deep neural network estimator for image classification
use an estimator to predict image labels
describe why deep neural networks don't work well with images
"define how neural networks work "
recall basics of image classification using neural networks
define the role of convolutional and pooling layers in a convolutional neural network
**Course Number:**it_sdaidt_03_enus

**Expertise Level**
Intermediate