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Get started with TensorFlow fundamentals to build and train deep
learning models with real-world data, practical exercises, and
challenging activities Key Features Understand the fundamentals of
tensors, neural networks, and deep learning Discover how to
implement and fine-tune deep learning models for real-world
datasets Build your experience and confidence with hands-on
exercises and activities Book DescriptionGetting to grips with
tensors, deep learning, and neural networks can be intimidating and
confusing for anyone, no matter their experience level. The breadth
of information out there, often written at a very high level and
aimed at advanced practitioners, can make getting started even more
challenging. If this sounds familiar to you, The TensorFlow
Workshop is here to help. Combining clear explanations, realistic
examples, and plenty of hands-on practice, it'll quickly get you up
and running. You'll start off with the basics - learning how to
load data into TensorFlow, perform tensor operations, and utilize
common optimizers and activation functions. As you progress, you'll
experiment with different TensorFlow development tools, including
TensorBoard, TensorFlow Hub, and Google Colab, before moving on to
solve regression and classification problems with sequential
models. Building on this solid foundation, you'll learn how to tune
models and work with different types of neural network, getting
hands-on with real-world deep learning applications such as text
encoding, temperature forecasting, image augmentation, and audio
processing. By the end of this deep learning book, you'll have the
skills, knowledge, and confidence to tackle your own ambitious deep
learning projects with TensorFlow. What you will learn Get to grips
with TensorFlow's mathematical operations Pre-process a wide
variety of tabular, sequential, and image data Understand the
purpose and usage of different deep learning layers Perform
hyperparameter-tuning to prevent overfitting of training data Use
pre-trained models to speed up the development of learning models
Generate new data based on existing patterns using generative
models Who this book is forThis TensorFlow book is for anyone who
wants to develop their understanding of deep learning and get
started building neural networks with TensorFlow. Basic knowledge
of Python programming and its libraries, as well as a general
understanding of the fundamentals of data science and machine
learning, will help you grasp the topics covered in this book more
easily.
Gain expert guidance on how to successfully develop machine
learning models in Python and build your own unique data platforms
Key Features Gain a full understanding of the model production and
deployment process Build your first machine learning model in just
five minutes and get a hands-on machine learning experience
Understand how to deal with common challenges in data science
projects Book DescriptionWhere there's data, there's insight. With
so much data being generated, there is immense scope to extract
meaningful information that'll boost business productivity and
profitability. By learning to convert raw data into game-changing
insights, you'll open new career paths and opportunities. The Data
Science Workshop begins by introducing different types of projects
and showing you how to incorporate machine learning algorithms in
them. You'll learn to select a relevant metric and even assess the
performance of your model. To tune the hyperparameters of an
algorithm and improve its accuracy, you'll get hands-on with
approaches such as grid search and random search. Next, you'll
learn dimensionality reduction techniques to easily handle many
variables at once, before exploring how to use model ensembling
techniques and create new features to enhance model performance. In
a bid to help you automatically create new features that improve
your model, the book demonstrates how to use the automated feature
engineering tool. You'll also understand how to use the
orchestration and scheduling workflow to deploy machine learning
models in batch. By the end of this book, you'll have the skills to
start working on data science projects confidently. By the end of
this book, you'll have the skills to start working on data science
projects confidently. What you will learn Explore the key
differences between supervised learning and unsupervised learning
Manipulate and analyze data using scikit-learn and pandas libraries
Understand key concepts such as regression, classification, and
clustering Discover advanced techniques to improve the accuracy of
your model Understand how to speed up the process of adding new
features Simplify your machine learning workflow for production Who
this book is forThis is one of the most useful data science books
for aspiring data analysts, data scientists, database engineers,
and business analysts. It is aimed at those who want to kick-start
their careers in data science by quickly learning data science
techniques without going through all the mathematics behind machine
learning algorithms. Basic knowledge of the Python programming
language will help you easily grasp the concepts explained in this
book.
Start with the basics of reinforcement learning and explore deep
learning concepts such as deep Q-learning, deep recurrent
Q-networks, and policy-based methods with this practical guide Key
Features Use TensorFlow to write reinforcement learning agents for
performing challenging tasks Learn how to solve finite Markov
decision problems Train models to understand popular video games
like Breakout Book DescriptionVarious intelligent applications such
as video games, inventory management software, warehouse robots,
and translation tools use reinforcement learning (RL) to make
decisions and perform actions that maximize the probability of the
desired outcome. This book will help you to get to grips with the
techniques and the algorithms for implementing RL in your machine
learning models. Starting with an introduction to RL, you'll be
guided through different RL environments and frameworks. You'll
learn how to implement your own custom environments and use OpenAI
baselines to run RL algorithms. Once you've explored classic RL
techniques such as Dynamic Programming, Monte Carlo, and TD
Learning, you'll understand when to apply the different deep
learning methods in RL and advance to deep Q-learning. The book
will even help you understand the different stages of machine-based
problem-solving by using DARQN on a popular video game Breakout.
Finally, you'll find out when to use a policy-based method to
tackle an RL problem. By the end of The Reinforcement Learning
Workshop, you'll be equipped with the knowledge and skills needed
to solve challenging problems using reinforcement learning. What
you will learn Use OpenAI Gym as a framework to implement RL
environments Find out how to define and implement reward function
Explore Markov chain, Markov decision process, and the Bellman
equation Distinguish between Dynamic Programming, Monte Carlo, and
Temporal Difference Learning Understand the multi-armed bandit
problem and explore various strategies to solve it Build a deep Q
model network for playing the video game Breakout Who this book is
forIf you are a data scientist, machine learning enthusiast, or a
Python developer who wants to learn basic to advanced deep
reinforcement learning algorithms, this workshop is for you. A
basic understanding of the Python language is necessary.
Take a hands-on approach to understanding deep learning and build
smart applications that can recognize images and interpret text Key
Features Understand how to implement deep learning with TensorFlow
and Keras Learn the fundamentals of computer vision and image
recognition Study the architecture of different neural networks
Book Description Are you fascinated by how deep learning powers
intelligent applications such as self-driving cars, virtual
assistants, facial recognition devices, and chatbots to process
data and solve complex problems? Whether you are familiar with
machine learning or are new to this domain, The Deep Learning
Workshop will make it easy for you to understand deep learning with
the help of interesting examples and exercises throughout. The book
starts by highlighting the relationship between deep learning,
machine learning, and artificial intelligence and helps you get
comfortable with the TensorFlow 2.0 programming structure using
hands-on exercises. You'll understand neural networks, the
structure of a perceptron, and how to use TensorFlow to create and
train models. The book will then let you explore the fundamentals
of computer vision by performing image recognition exercises with
convolutional neural networks (CNNs) using Keras. As you advance,
you'll be able to make your model more powerful by implementing
text embedding and sequencing the data using popular deep learning
solutions. Finally, you'll get to grips with bidirectional
recurrent neural networks (RNNs) and build generative adversarial
networks (GANs) for image synthesis. By the end of this deep
learning book, you'll have learned the skills essential for
building deep learning models with TensorFlow and Keras. What you
will learn Understand how deep learning, machine learning, and
artificial intelligence are different Develop multilayer deep
neural networks with TensorFlow Implement deep neural networks for
multiclass classification using Keras Train CNN models for image
recognition Handle sequence data and use it in conjunction with
RNNs Build a GAN to generate high-quality synthesized images Who
this book is for If you are interested in machine learning and want
to create and train deep learning models using TensorFlow and
Keras, this workshop is for you. A solid understanding of Python
and its packages, along with basic machine learning concepts, will
help you to learn the topics quickly.
With knowledge and information shared by experts, take your first
steps towards creating scalable AI algorithms and solutions in
Python, through practical exercises and engaging activities Key
Features Learn about AI and ML algorithms from the perspective of a
seasoned data scientist Get practical experience in ML algorithms,
such as regression, tree algorithms, clustering, and more Design
neural networks that emulate the human brain Book DescriptionYou
already know that artificial intelligence (AI) and machine learning
(ML) are present in many of the tools you use in your daily
routine. But do you want to be able to create your own AI and ML
models and develop your skills in these domains to kickstart your
AI career? The Applied Artificial Intelligence Workshop gets you
started with applying AI with the help of practical exercises and
useful examples, all put together cleverly to help you gain the
skills to transform your career. The book begins by teaching you
how to predict outcomes using regression. You'll then learn how to
classify data using techniques such as k-nearest neighbor (KNN) and
support vector machine (SVM) classifiers. As you progress, you'll
explore various decision trees by learning how to build a reliable
decision tree model that can help your company find cars that
clients are likely to buy. The final chapters will introduce you to
deep learning and neural networks. Through various activities, such
as predicting stock prices and recognizing handwritten digits,
you'll learn how to train and implement convolutional neural
networks (CNNs) and recurrent neural networks (RNNs). By the end of
this applied AI book, you'll have learned how to predict outcomes
and train neural networks and be able to use various techniques to
develop AI and ML models. What you will learn Create your first AI
game in Python with the minmax algorithm Implement regression
techniques to simplify real-world data Experiment with
classification techniques to label real-world data Perform
predictive analysis in Python using decision trees and random
forests Use clustering algorithms to group data without manual
support Learn how to use neural networks to process and classify
labeled images Who this book is forThe Applied Artificial
Intelligence Workshop is designed for software developers and data
scientists who want to enrich their projects with machine learning.
Although you do not need any prior experience in AI, it is
recommended that you have knowledge of high school-level
mathematics and at least one programming language, preferably
Python. Although this is a beginner's book, experienced students
and programmers can improve their Python skills by implementing the
practical applications given in this book.
Cut through the noise and get real results with a step-by-step
approach to data science Key Features Ideal for the data science
beginner who is getting started for the first time A data science
tutorial with step-by-step exercises and activities that help build
key skills Structured to let you progress at your own pace, on your
own terms Use your physical print copy to redeem free access to the
online interactive edition Book DescriptionYou already know you
want to learn data science, and a smarter way to learn data science
is to learn by doing. The Data Science Workshop focuses on building
up your practical skills so that you can understand how to develop
simple machine learning models in Python or even build an advanced
model for detecting potential bank frauds with effective modern
data science. You'll learn from real examples that lead to real
results. Throughout The Data Science Workshop, you'll take an
engaging step-by-step approach to understanding data science. You
won't have to sit through any unnecessary theory. If you're short
on time you can jump into a single exercise each day or spend an
entire weekend training a model using sci-kit learn. It's your
choice. Learning on your terms, you'll build up and reinforce key
skills in a way that feels rewarding. Every physical print copy of
The Data Science Workshop unlocks access to the interactive
edition. With videos detailing all exercises and activities, you'll
always have a guided solution. You can also benchmark yourself
against assessments, track progress, and receive content updates.
You'll even earn a secure credential that you can share and verify
online upon completion. It's a premium learning experience that's
included with your printed copy. To redeem, follow the instructions
located at the start of your data science book. Fast-paced and
direct, The Data Science Workshop is the ideal companion for data
science beginners. You'll learn about machine learning algorithms
like a data scientist, learning along the way. This process means
that you'll find that your new skills stick, embedded as best
practice. A solid foundation for the years ahead. What you will
learn Find out the key differences between supervised and
unsupervised learning Manipulate and analyze data using
scikit-learn and pandas libraries Learn about different algorithms
such as regression, classification, and clustering Discover
advanced techniques to improve model ensembling and accuracy Speed
up the process of creating new features with automated feature tool
Simplify machine learning using open source Python packages Who
this book is forOur goal at Packt is to help you be successful, in
whatever it is you choose to do. The Data Science Workshop is an
ideal data science tutorial for the data science beginner who is
just getting started. Pick up a Workshop today and let Packt help
you develop skills that stick with you for life.
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