Get up to speed with machine learning techniques and create smart solutions for different problems
- Master supervised, unsupervised, and semi-supervised machine learning algorithms and their implementation.
- Build deep learning models for object detection, image classification, and similarity learning.
- Develop, deploy, and scale end-to-end deep neural network models in a production environment.
Gaining expertise in artificial intelligence requires an in-depth understanding of the most popular machine learning algorithms. With this book, you’ll be able to explore the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning and learn how to use them most effectively. From Bayesian models to the MCMC algorithm and even Hidden Markov models, this Learning Path will teach you to extract features from your dataset and perform dimensionality reduction using Python-based libraries.
You’ll use TensorFlow and Keras to build deep learning models with concepts like transfer learning, generative adversarial networks, and deep reinforcement learning. Next, you’ll discover TensorFlow1.x’s advanced features, such as distributed TensorFlow with TF clusters and understand the deployment of production models with TensorFlow Serving. As you progress, the book will guide you on implementing techniques related to object classification, object detection, and image segmentation.
By the end of this Python book, you’ll have gained in-depth knowledge of TensorFlow and the skills you need to solve artificial intelligence problems.
This Learning Path includes content from the following Packt books:
- 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
- Get up to speed with how a machine model can be trained, optimized, and evaluated.
- Work with autoencoders and generative adversarial networks
- Explore the most essential reinforcement learning techniques
- Build end-to-end deep learning (CNN, RNN, and autoencoder) models
- Define and train a model for image and video classification
- Deploy your deep learning models and optimize them for high-performance
Who this book is for
This Learning Path is for data scientists, machine learning engineers, and artificial intelligence engineers who want to delve into complex machine learning algorithms, calibrate models, and improve predictions of trained models. Basic Python programming and machine learning concepts are required to get the most out of this book.
You might also be interested in my Technical Posts, which contain addenda and related topics.
"The limits of my language means the limits of my world.” (L. Wittgenstein) When Jacques Lacan proposed his psychoanalytical theory based on the influence of language on human beings, many auditors remained initially astonished. Is…
Hebbian Learning is one of the most famous learning theories. It was proposed by the Canadian psychologist Donald Hebb in 1949, many years before his results were confirmed through neuroscientific experiments. Artificial Intelligence researchers immediately…
Stochastic Gradient Descent (SGD) is a potent technique currently employed to optimize all deep learning models. However, the vanilla algorithm has many limitations, mainly when the system is ill-conditioned and can never find the global…
Other Books From - Technical Books
Other Books By - Giuseppe Bonaccorso