Deep Learning - Convolutional Neural Networks with TensorFlow: Master Machine Learning and Neural Networks for Data Science and Computer Vision

Lazy Programmer

$109.99

Video Feb 2023 3hrs 39mins 1st Edition

Lazy Programmer

$109.99

Video Feb 2023 3hrs 39mins 1st Edition

What do you get with a video?

Download this video in MP4 format

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

View table of contents

Key benefits

Learn how to use TensorFlow 2 to build Convolutional Neural Networks (CNNs)
The course covers Natural Language Processing (NLP) and transfer learning for Computer Vision
Explains how to apply CNNs to NLP

Description

TensorFlow is the world’s most popular library for deep learning, and it is built by Google. It is the library of choice for many companies doing AI (Artificial Intelligence) and machine learning. So, if you want to do deep learning, you must know TensorFlow. In this course, you will learn how to use TensorFlow 2 to build convolutional neural networks (CNN). We will first start by having an in-depth look at what convolution is, why it is useful, and how to integrate it into a neural network. Then you will learn how to apply CNNs to several practical image recognition datasets, from small and relatively simple to large and complex. Next, you will learn how to perform text preprocessing and text classification with CNNs. In the last section, you will learn about techniques that help improve performance, such as batch normalization, data augmentation, and transfer learning for Computer Vision. By the end of this course, we will have understood how to build convolutional neural networks in deep learning with TensorFlow.

Who is this book for?

This course is designed for anyone interested in deep learning and machine learning or for anyone who wants to implement convolutional neural networks in TensorFlow 2. One must have decent Python programming skills, should know how to build a feedforward ANN (Artificial Neural Network) in TensorFlow 2, and must have experience with data science libraries such as NumPy and Matplotlib.