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Getting Started with TensorFlow 2.0 for Deep Learning [Video]

Getting Started with TensorFlow 2.0 for Deep Learning: Leverage the power of deep learning with TensorFlow 2.0 [Video]

By Muhammad Hamza Javed
$130.99
Video Aug 2019 1 hours 54 minutes 1st Edition
Video
$130.99
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$15.99 Monthly
Video
$130.99
Subscription
$15.99 Monthly

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Product Details

Publication date : Aug 22, 2019
Length 1 hours 54 minutes
Edition : 1st Edition
Language : English
ISBN-13 : 9781789954470
Vendor :
Google
Category :
Data
Languages :
Python (Intermediate)
Concepts :
Deep Learning
Tools :
TensorFlow (Beginner)
toc View table of contents toc Download Code

Key benefits

  • Explore the latest feature set and modern deep learning APIs in TensorFlow 2.0
  • Develop computer vision and text sequences based on deep learning models
  • Learn advanced deep learning topics including Keras functional API

Description

Deep learning is a trending technology if you want to break into cutting-edge AI and solve real-world, data-driven problems. Google’s TensorFlow is a popular library for implementing deep learning algorithms because of its rapid developments and commercial deployments. This course provides you with the core of deep learning using TensorFlow 2.0. You’ll learn to train your deep learning networks from scratch, pre-process and split your datasets, train deep learning models for real-world applications, and validate the accuracy of your models. By the end of the course, you’ll have a profound knowledge of how you can leverage TensorFlow 2.0 to build real-world applications without much effort. All the notebooks and supporting files for this course are available on GitHub at https://github.com/PacktPublishing/Getting-Started-with-TensorFlow-2.0-for-Deep-Learning-Video

What you will learn

Develop real-world deep learning applications Classify IMDb Movie Reviews using Binary Classification Model Build a model to classify news with multi-label Train your deep learning model to predict house prices Understand the whole package: prepare a dataset, build the deep learning model, and validate results Understand the working of Recurrent Neural Networks and LSTM with hands-on examples Implement autoencoders and denoise autoencoders in a project to regenerate images

What do you get with a video?

Feature icon Download this video in MP4 format
Feature icon Access this title in our online reader with advanced features
Feature icon DRM FREE - Read whenever, wherever and however you want
Buy Now

Product Details

Publication date : Aug 22, 2019
Length 1 hours 54 minutes
Edition : 1st Edition
Language : English
ISBN-13 : 9781789954470
Vendor :
Google
Category :
Data
Languages :
Python (Intermediate)
Concepts :
Deep Learning
Tools :
TensorFlow (Beginner)

Table of Contents

7 Chapters
Deep Learning Basics Packt Packt
The Course Overview
Introducing Deep Learning and Setting Up the Environment
Introducing the Universal Workflow of Deep Learning
Mathematics Refresher
Training, Validation, and Test Sets
Data Preprocessing and Feature Engineering for Deep Learning Models
Demonstrating Overfitting and Underfitting of Data
TensorFlow 2.0 for Deep Learning Packt Packt
TensorFlow 2.0 Benefits and New Features
Neural Networks in TensorFlow 2.0
Getting and Preprocessing MNIST Dataset
Building Neural Network for Classifying CIFAR10 Dataset in Jupyter Notebooks
TensorBoard and Callbacks
Working with CNNs for Computer Vision and Deep Learning Packt Packt
Introduction to Convolutional Neural Networks
Loading the Dataset and Making Train and Test Sets
Dataset Preprocessing
Building CNN Model from Scratch with the Home Objects Dataset
Data Augmentation to Overcome Overfitting and Generate Similar Images
Working with LSTM for Text Data and Deep Learning Packt Packt
Introducing LSTM and Recurrent Neural Networks
Downloading and Working Textual Data: N-Grams and Bag-Of-Words
Using GloVe Word Embeddings
Building an LSTM Model for Text Data and Getting the Results
Working with RNNs for Time Series Sequences and Deep Learning Packt Packt
Introducing Recurrent Neural Networks
Getting and Preprocessing the Temperature Data
Building an RNN Model for Temperature Forecasting
Autoencoders – AE and Denoising AE Packt Packt
What Are Autoencoders? Why Are They Used?
Creating Simple Autoencoders from Scratch with the Fashion-MNIST Dataset
Going in Depth with Deep Autoencoders
Reconstruct Images with Denoising Autoencoders
Deep Learning Mini-Projects Packt Packt
TensorFlow 2.0 High Level API and Keras-API
Getting and Preprocessing IMDB Dataset for Binary Classification
Dataset Preparation, Building, and Validating Results on the IMDB Dataset
Getting and Preprocessing Reuters Dataset for Multi-Label Classification
Dataset Preparation, Building, and Validating Results on the Reuters Dataset
Getting and Preprocessing Dataset for Regression Analysis
Dataset Preparation on the Boston Housing Price Dataset

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Authors (1)

Profile Muhammad Hamza Javed
Linkedin Github
Muhammad Hamza Javed is a self-taught machine learning engineer, an entrepreneur, and an author with over five years of industrial experience. Along with his team, he has been working on several computer vision, machine learning, and deep learning international projects. He learned skills on his own without a direct mentor, so he knows how troublesome it is for everyone to find to-the-point content that improves ones skillset. Hes designed this course considering the challenges he faced when he learned and in projects, so you dont have to spend too much time finding whats best for you.
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