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Applied Deep Learning with TensorFlow

$74,99 per month

SUBSCRIPTION FOR 4 weeks, 2 days

Description

Summary
TensorFlow* is a popular machine learning framework and open source library for dataflow programming. In this course, you will learn about:

The fundamentals of building models with TensorFlow
Machine learning basics like linear regression, loss functions, and gradient descent
Important techniques like normalization, regularization, and minibatching
Kernels and how to apply them to convolutional neural networks (CNN)
The basic template for a CNN and different parameters that can be adjusted
TFRecord, queues, and coordinators
By the end of this course, students will have a firm understanding of:

Basic network construction, kernels, pooling, and multiclass classification
How to expand a basic network into a more complex network
Using transfer learning to take advantage of existing networks by building on top of them
The course is structured around eight week of lectures and exercises.

What you’ll learn
Best practices for TensorFlow, a popular open-source machine learning framework to train a neural network for a computer vision applications.

Handle real-world image data and explore strategies to prevent overfitting, including augmentation and dropout.

Build natural language processing systems using TensorFlow.

Apply RNNs, GRUs, and LSTMs as you train them using text repositories.

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