Develop real-world applications powered by the latest advances in intelligent systems

Key Features

Gain real-world contextualization using deep learning problems concerning research and application

Get to know the best practices to improve and optimize your machine learning systems and algorithms

Design and implement machine intelligence using real-world AI-based examples

Book Description

This Learning Path offers practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis. You will be introduced to various machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. You will learn to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open-source Python libraries.

Throughout the Learning Path, you'll learn how to develop deep learning applications for machine learning systems. Discover how to attain deep learning programming on GPU in a distributed way.

By the end of this Learning Path, you know the fundamentals of AI and have worked through a number of case studies that will help you apply your skills to real-world projects.

This Learning Path includes content from the following Packt products:

Artificial Intelligence By Example by Denis Rothman

Python Deep Learning Projects by Matthew Lamons, Rahul Kumar, and Abhishek Nagaraja

Hands-On Artificial Intelligence with TensorFlow by Amir Ziai, Ankit Dixit

What you will learn

Use adaptive thinking to solve real-life AI case studies

Rise beyond being a modern-day factory code worker

Understand future AI solutions and adapt quickly to them

Master deep neural network implementation using TensorFlow

Predict continuous target outcomes using regression analysis

Dive deep into textual and social media data using sentiment analysis

Who this book is for

This Learning Path is for anyone who wants to understand the fundamentals of Artificial Intelligence and implement it practically by devising smart solutions. You will learn to extend your machine learning and deep learning knowledge by creating practical AI smart solutions. Prior experience with Python and statistical knowledge is essential to make the most out of this Learning Path.

Table of Contents

Become an Adaptive Thinker

Think Like a Machine

Apply Machine Thinking to a Human Problem

Become an Unconventional Innovator

Manage the Power of Machine Learning and Deep Learning

Focus on Optimizing Your Solutions

When and How to Use Artificial Intelligence

Revolutions Designed for Some Corporations and Disruptive Innovations for Small to Large Companies

Getting Your Neurons to Work

Applying Biomimicking to Artificial Intelligence

Conceptual Representation Learning

Optimizing Blockchains with AI

Cognitive NLP Chatbots

Improve the Emotional Intelligence Deficiencies of Chatbots

Building Deep Learning Environments

Training NN for Prediction Using Regression

Generative Language Model for Content Creation

Building Speech Recognition with DeepSpeech2

Handwritten Digits Classification Using ConvNets

Object Detection Using OpenCV and TensorFlow

Building Face Recognition Using FaceNet

Generative Adversarial Networks

From GPUs to Quantum computing - AI Hardware

TensorFlow Serving

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