Demystify the complexity of machine learning techniques and create
evolving, clever solutions to solve your problems Key Features
Master supervised, unsupervised, and semi-supervised ML algorithms
and their implementation Build deep learning models for object
detection, image classification, similarity learning, and more
Build, deploy, and scale end-to-end deep neural network models in a
production environment Book DescriptionThis Learning Path is your
complete guide to quickly getting to grips with popular machine
learning algorithms. You'll be introduced to the most widely used
algorithms in supervised, unsupervised, and semi-supervised machine
learning, and learn how to use them in the best possible manner.
Ranging from Bayesian models to the MCMC algorithm to Hidden Markov
models, this Learning Path will teach you how to extract features
from your dataset and perform dimensionality reduction by making
use of Python-based libraries. You'll bring the use of TensorFlow
and Keras to build deep learning models, using concepts such as
transfer learning, generative adversarial networks, and deep
reinforcement learning. Next, you'll learn the advanced features of
TensorFlow1.x, such as distributed TensorFlow with TF clusters,
deploy production models with TensorFlow Serving. You'll implement
different techniques related to object classification, object
detection, image segmentation, and more. By the end of this
Learning Path, you'll have obtained in-depth knowledge of
TensorFlow, making you the go-to person for solving artificial
intelligence problems This Learning Path includes content from the
following Packt products: Mastering Machine Learning Algorithms by
Giuseppe Bonaccorso Mastering TensorFlow 1.x by Armando Fandango
Deep Learning for Computer Vision by Rajalingappaa Shanmugamani
What you will learn Explore how an ML model can be trained,
optimized, and evaluated Work with Autoencoders and Generative
Adversarial Networks Explore the most important Reinforcement
Learning techniques Build end-to-end deep learning (CNN, RNN, and
Autoencoders) models Who this book is forThis Learning Path is for
data scientists, machine learning engineers, artificial
intelligence engineers who want to delve into complex machine
learning algorithms, calibrate models, and improve the predictions
of the trained model. You will encounter the advanced intricacies
and complex use cases of deep learning and AI. A basic knowledge of
programming in Python and some understanding of machine learning
concepts are required to get the best out of this Learning Path.
General
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