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Proteomics is a well-established area of Science; yet with a strong
area in constant evolution, namely sample treatment. There few
books that currently cover the field of emerging sample treatments
in proteomics, this new volume will be the first to cover all
emerging and existing studies. This unique book presents the latest
advances in the field focusing on emerging trends linked to
high-resolution mass spectrometry, technology addressed to treat
samples faster and to attempts to simplify the proteome for the
reader.
Proteomics is a well-established area of Science; yet with a strong
area in constant evolution, namely sample treatment. There few
books that currently cover the field of emerging sample treatments
in proteomics, this new volume will be the first to cover all
emerging and existing studies. This unique book presents the latest
advances in the field focusing on emerging trends linked to
high-resolution mass spectrometry, technology addressed to treat
samples faster and to attempts to simplify the proteome for the
reader.
Cut through the noise and get real results with this workshop for
beginners. Use a project-based approach to exploring machine
learning with TensorFlow and Keras. Key Features Understand the
nuances of setting up a deep learning programming environment Gain
insights into the common components of a neural network and its
essential operations Get to grips with deploying a machine learning
model as an interactive web application with Flask Book
DescriptionMachine learning gives computers the ability to learn
like humans. It is becoming increasingly transformational to
businesses in many forms, and a key skill to learn to prepare for
the future digital economy. As a beginner, you'll unlock a world of
opportunities by learning the techniques you need to contribute to
the domains of machine learning, deep learning, and modern data
analysis using the latest cutting-edge tools. The Applied
TensorFlow and Keras Workshop begins by showing you how neural
networks work. After you've understood the basics, you will train a
few networks by altering their hyperparameters. To build on your
skills, you'll learn how to select the most appropriate model to
solve the problem in hand. While tackling advanced concepts, you'll
discover how to assemble a deep learning system by bringing
together all the essential elements necessary for building a basic
deep learning system - data, model, and prediction. Finally, you'll
explore ways to evaluate the performance of your model, and improve
it using techniques such as model evaluation and hyperparameter
optimization. By the end of this book, you'll have learned how to
build a Bitcoin app that predicts future prices, and be able to
build your own models for other projects. What you will learn
Familiarize yourself with the components of a neural network
Understand the different types of problems that can be solved using
neural networks Explore different ways to select the right
architecture for your model Make predictions with a trained model
using TensorBoard Discover the components of Keras and ways to
leverage its features in your model Explore how you can deal with
new data by learning ways to retrain your model Who this book is
forIf you are a data scientist or a machine learning and deep
learning enthusiast, who is looking to design, train, and deploy
TensorFlow and Keras models into real-world applications, then this
workshop is for you. Knowledge of computer science and machine
learning concepts and experience in analyzing data will help you to
understand the topics explained in this book with ease.
A hands-on guide to deep learning that's filled with intuitive
explanations and engaging practical examples Key Features Designed
to iteratively develop the skills of Python users who don't have a
data science background Covers the key foundational concepts you'll
need to know when building deep learning systems Full of
step-by-step exercises and activities to help build the skills that
you need for the real-world Book DescriptionTaking an approach that
uses the latest developments in the Python ecosystem, you'll first
be guided through the Jupyter ecosystem, key visualization
libraries and powerful data sanitization techniques before we train
our first predictive model. We'll explore a variety of approaches
to classification like support vector networks, random decision
forests and k-nearest neighbours to build out your understanding
before we move into more complex territory. It's okay if these
terms seem overwhelming; we'll show you how to put them to work.
We'll build upon our classification coverage by taking a quick look
at ethical web scraping and interactive visualizations to help you
professionally gather and present your analysis. It's after this
that we start building out our keystone deep learning application,
one that aims to predict the future price of Bitcoin based on
historical public data. By guiding you through a trained neural
network, we'll explore common deep learning network architectures
(convolutional, recurrent, generative adversarial) and branch out
into deep reinforcement learning before we dive into model
optimization and evaluation. We'll do all of this whilst working on
a production-ready web application that combines Tensorflow and
Keras to produce a meaningful user-friendly result, leaving you
with all the skills you need to tackle and develop your own
real-world deep learning projects confidently and effectively. What
you will learn Discover how you can assemble and clean your very
own datasets Develop a tailored machine learning classification
strategy Build, train and enhance your own models to solve unique
problems Work with production-ready frameworks like Tensorflow and
Keras Explain how neural networks operate in clear and simple terms
Understand how to deploy your predictions to the web Who this book
is forIf you're a Python programmer stepping into the world of data
science, this is the ideal way to get started.
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