“…What you will learn Explore how to use tokenizers in NLP processing Implement NLP techniques in machine learning and deep learning applications Identify sentences within text and learn how to train specialized NER models Learn how to classify documents and perform sentiment analysis Find semantic similarities between text elements and extract text from a variety of sources Preprocess text from a variety of data sources Learn how to identify and translate languages Who this book is for This book is for data scientists, NLP engineers, and machine learning developers who want to perform their work on linguistic applications faster with the use of popular libraries on JVM machines. …”

“…What you will learn Understand key characteristics of IBM machine learning services Run supervised and unsupervised techniques in the cloud Understand how to create a Spark pipeline in Watson Studio Implement deep learning and neural networks on the IBM Cloud with TensorFlow Create a complete, cloud-based facial expression classification solution Use biometric traits to build a cloud-based human identification system Who this book is for This beginner-level book is for data scientists and machine learning engineers who want to get started with IBM Cloud and its machine learning services using practical examples. …”

“…What you will learn Understand the theory and concepts behind modern Reinforcement Learning algorithms Code state-of-the-art Reinforcement Learning algorithms with discrete or continuous actions Develop Reinforcement Learning algorithms and apply them to training agents to play computer games Explore DQN, DDQN, and Dueling architectures to play Atari's Breakout using TensorFlow Use A3C to play CartPole and LunarLander Train an agent to drive a car autonomously in a simulator Who this book is for Data scientists and AI developers who wish to quickly get started with training effective reinforcement learning models in TensorFlow will find this book very useful. …”

“…What you will learn Practice the Markov decision process in prediction and betting evaluations Implement Monte Carlo methods to forecast environment behaviors Explore TD learning algorithms to manage warehouse operations Construct a Deep Q-Network using Python and Keras to control robot movements Apply reinforcement concepts to build a handwritten digit recognition model using an image dataset Address a game theory problem using Q-Learning and OpenAI Gym Who this book is for Keras Reinforcement Learning Projects is for you if you are data scientist, machine learning developer, or AI engineer who wants to understand the fundamentals of reinforcement learning by developing practical projects. …”

“…What you will learn Understand the TensorFlow ecosystem using various datasets and techniques Create recommendation systems for quality product recommendations Build projects using CNNs, NLP, and Bayesian neural networks Play Pac-Man using deep reinforcement learning Deploy scalable TensorFlow-based machine learning systems Generate your own book script using RNNs Who this book is for TensorFlow Machine Learning Projects is for you if you are a data analyst, data scientist, machine learning professional, or deep learning enthusiast with basic knowledge of TensorFlow. …”

“…What you will learn Train and evaluate neural networks built using TensorFlow for RL Use RL algorithms in Python and TensorFlow to solve CartPole balancing Create deep reinforcement learning algorithms to play Atari games Deploy RL algorithms using OpenAI Universe Develop an agent to chat with humans Implement basic actor-critic algorithms for continuous control Apply advanced deep RL algorithms to games such as Minecraft Autogenerate an image classifier using RL Who this book is for Python Reinforcement Learning Projects is for data analysts, data scientists, and machine learning professionals, who have working knowledge of machine learning techniques and are looking to build better performing, automated, and optimized deep learning models. …”

“…What you will learn Explore deep neural networks and various frameworks that can be used in R Develop a joke recommendation engine to recommend jokes that match users' tastes Create powerful ML models with ensembles to predict employee attrition Build autoencoders for credit card fraud detection Work with image recognition and convolutional neural networks Make predictions for casino slot machine using reinforcement learning Implement NLP techniques for sentiment analysis and customer segmentation Who this book is for If you're a data analyst, data scientist, or machine learning developer who wants to master machine learning concepts using R by building real-world projects, this is the book for you. …”

“…What you will learn Obtain, verify, and clean data before transforming it into a correct format for use Perform data analysis and machine learning tasks using Python Understand the basics of computational linguistics Build models for general natural language processing tasks Evaluate the performance of a model with the right metrics Visualize, quantify, and perform exploratory analysis from any text data Who this book is for Natural Language Processing Fundamentals is designed for novice and mid-level data scientists and machine learning developers who want to gather and analyze text data to build an NLP-powered product. …”

“…What you will learn Build multiple advanced neural network architectures from scratch Explore transfer learning to perform object detection and classification Build self-driving car applications using instance and semantic segmentation Understand data encoding for image, text and recommender systems Implement text analysis using sequence-to-sequence learning Leverage a combination of CNN and RNN to perform end-to-end learning Build agents to play games using deep Q-learning Who this book is for This intermediate-level book targets beginners and intermediate-level machine learning practitioners and data scientists who have just started their journey with neural networks. …”

“…What you will learn Use and manipulate complex and simple data structures Harness the full potential of DataFrames and numpy.array at run time Perform web scraping with BeautifulSoup4 and html5lib Execute advanced string search and manipulation with RegEX Handle outliers and perform data imputation with Pandas Use descriptive statistics and plotting techniques Practice data wrangling and modeling using data generation techniques Who this book is for Data Wrangling with Python is designed for developers, data analysts, and business analysts who are keen to pursue a career as a full-fledged data scientist or analytics expert. Although, this book is for beginners, prior working knowledge of Python is necessary to easily grasp the concepts covered here. …”

“…What you will learn Explore deep learning models and implement them in your browser Design a smart web-based client using Django and Flask Work with different Python-based APIs for performing deep learning tasks Implement popular neural network models with TensorFlow.js Design and build deep web services on the cloud using deep learning Get familiar with the standard workflow of taking deep learning models into production Who this book is for This deep learning book is for data scientists, machine learning practitioners, and deep learning engineers who are looking to perform deep learning techniques and methodologies on the web. …”

“…What you will learn Develop a joke recommendation engine to show jokes that match users’ tastes Build autoencoders for credit card fraud detection Work with image recognition and convolutional neural networks Make predictions for casino slot machines using reinforcement learning Implement natural language processing (NLP) techniques for sentiment analysis and customer segmentation Produce simple and effective data visualizations for improved insights Use NLP to extract insights for text Implement tree-based classifiers including random forest and boosted tree Who this book is for If you're a data analyst, data scientist, or machine learning developer who wants to master machine learning techniques using R, this is an ideal Learning Path for you. …”

“…What you will learn Implement credit card fraud detection with autoencoders Train neural networks to perform handwritten digit recognition using MXNet Reconstruct images using variational autoencoders Explore the applications of autoencoder neural networks in clustering and dimensionality reduction Create natural language processing (NLP) models using Keras and TensorFlow in R Prevent models from overfitting the data to improve generalizability Build shallow neural network prediction models Who this book is for This Learning Path is for aspiring data scientists, data analysts, machine learning developers, and deep learning enthusiasts who are well versed in machine learning concepts and are looking to explore the deep learning paradigm using R. …”

“…5 real-world projects to help you master deep learning concepts About This Book Master the different deep learning paradigms and build real-world projects related to text generation, sentiment analysis, fraud detection, and more Get to grips with R's impressive range of Deep Learning libraries and frameworks such as deepnet, MXNetR, Tensorflow, H2O, Keras, and text2vec Practical projects that show you how to implement different neural networks with helpful tips, tricks, and best practices Who This Book Is For Machine learning professionals and data scientists looking to master deep learning by implementing practical projects in R will find this book a useful resource. …”

“…What you will learn Use RNN models in Magenta to generate MIDI percussion, and monophonic and polyphonic sequences Use WaveNet and GAN models to generate instrument notes in the form of raw audio Employ Variational Autoencoder models like MusicVAE and GrooVAE to sample, interpolate, and humanize existing sequences Prepare and create your dataset on specific styles and instruments Train your network on your personal datasets and fix problems when training networks Apply MIDI to synchronize Magenta with existing music production tools like DAWs Who this book is for This book is for technically inclined artists and musically inclined computer scientists. Readers who want to get hands-on with building..…”

“…This Learning Path includes content from the following Packt products: Julia 1.0 Programming - Second Edition by Ivo Balbaert Julia Programming Projects by Adrian Salceanu What you will learn Create your own types to extend the built-in type system Visualize your data in Julia with plotting packages Explore the use of built-in macros for testing and debugging Integrate Julia with other languages such as C, Python, and MATLAB Analyze and manipulate datasets using Julia and DataFrames Develop and run a web app using Julia and the HTTP package Build a recommendation system using supervised machine learning Who this book is for If you are a statistician or data scientist who wants a quick course in the Julia programming language while building big data applications, this Learning Path is for you. …”

“…Control your machine learning algorithms using test-driven development to achieve quantifiable milestones About This Book Build smart extensions to pre-existing features at work that can help maximize their value Quantify your models to drive real improvement Take your knowledge of basic concepts, such as linear regression and Naïve Bayes classification, to the next level and productionalize their models Play what-if games with your models and techniques by following the test-driven exploration process Who This Book Is For This book is intended for data technologists (scientists, analysts, or developers) with previous machine learning experience who are also comfortable reading code in Python. …”

“…Master GUI programming in Tkinter as you design, implement, and deliver ten real-world applications from start to finish About This Book Conceptualize and build state-of-art GUI applications with Tkinter Tackle the complexity of just about any size GUI application with a structured and scalable approach A project-based, practical guide to get hands-on into Tkinter GUI development Who This Book Is For Software developers, scientists, researchers, engineers, students, or programming hobbyists with basic familiarity in Python will find this book interesting and informative. …”

“…Covering topics for the data scientist, Oracle developer, and Oracle database administrator, this Oracle Press guide shows you how to get started with Oracle Data Miner and build Oracle Data Miner models using SQL and PL/SQL packages. …”

“…Skill Level All Levels Beginner Intermediate Advanced What You Will Learn The essentials and basic terminology of predictive analysis How to use Excel's core predictive analysis tools, including the Data Analysis and Solver add-ins How to perform quantitative analyses using smoothing and regression How to create effective forecasts using autoregression How trends in time series work, and how to handle the challenges they create How to choose the best approach to forecast any time series Who Should Take This Course Every businessperson, scientist, analyst, and student who wants to master the essentials of predictive analytics Course Requirements Basic experience with Microsoft Excel Basic knowledge of simple statistical analysis techniques Table of Contents Introduction Part 1: Moving Averages Lesson 1: Leng..…”