Artificial Intelligence is especially relevant in today’s technology and data-driven world. It’s used in search engines, image recognition, robotics, finance, and many other industries. In this book, you’ll explore various real-world scenarios while learning about various algorithms that can be used to build AI applications. Starting out from the basics of AI, you’ll learn how to develop various building blocks using different data mining techniques before delving into more advanced subjects.
- Access 446 pages of digital content 24/7
- Explore different classification & regression techniques
- Understand the concept of clustering & how to use it to automatically segment data
- See how to build an intelligent recommender system
- Discover logic programming & how to use it
- Build automatic speech recognition systems
- Understand the basics of heuristic search & genetic programming
- Develop games using Artificial Intelligence
- Learn how reinforcement learning works
- Discover how to build intelligent applications centered on images, text, & time series data
- See how to use deep learning algorithms & build applications based on them
AI and deep learning are transforming the way we understand software, making computers more intelligent than we could imagine even a decade ago. Deep learning algorithms are being used across a broad range of industries to produce hardware like self-driving cars, personal assistant computers, and decision support systems. As the fundamental driver of AI, Java is becoming a more vital and valuable skill in the global economy, and this course will introduce you to using Java for deep learning.
- Access 20 lectures & 2 hours of content 24/7
- Install the environment precisely
- Use the DL4J & apply deep learning to a range of real-world use cases
- Get an introduction to neural networks & implementing them
- Learn about various deep learning algorithms
- Tune Apache Spark
AI and Deep Learning are transforming the way we understand software, making computers more intelligent than we could even imagine just a decade ago. Starting with an introduction to basic machine learning algorithms, this course takes you further into this vital world of stunning predictive insights and remarkable machine intelligence.
- Access 254 pages of content 24/7
- Get a practical deep dive into machine learning & deep learning algorithms
- Implement machine learning algorithms related to deep learning
- Explore neural networks using some of the most popular Deep Learning frameworks
- Dive into Deep Belief Nets & Stacked Denoising Autoencoders algorithms
- Discover more deep learning algorithms w/ Dropout & Convolutional Neural Networks
- Gain an insight into the deep learning library DL4J & its practical uses
- Get to know device strategies to use deep learning algorithms & libraries in the real world
- Explore deep learning further w/ Theano & Caffe
Deep learning is the step that comes after machine learning, and has more advanced implementations. Throughout this book, you’ll learn how to implement deep learning algorithms for machine learning systems and integrate them into your product offerings, including search, image recognition, and language processing. After finishing the book, you’ll be familiar with machine learning techniques, in particular the use of TensorFlow for deep learning.
- Access 320 pages of digital content 24/7
- Learn about machine learning landscapes along w/ the historical development & progress of deep learning
- Discuss deep machine intelligence & GPU computing w/ the latest TensorFlow 1.x
- Access public datasets & utilize them using TensorFlow to load, process, and transform data
- Use TensorFlow on real-world datasets, including images, text, & more
- Learn how to evaluate the performance of your deep learning models
- Use deep learning for scalable object detection & mobile computing
- Train machines quickly to learn from data by exploring reinforcement learning techniques
- Explore active areas of deep learning research & applications
Deep learning is the intersection of statistics, artificial intelligence, and data to build accurate models, and is one of the most important new frontiers in technology. TensorFlow is one of the newest and most comprehensive libraries for implementing deep learning. Over this course you’ll explore some of the possibilities of deep learning, and how to use TensorFlow to process data more effectively than ever.
- Access 22 lectures & 2 hours of content 24/7
- Discover the efficiency & simplicity of TensorFlow
- Process & change how you look at data
- Sift for hidden layers of abstraction using raw data
- Train your machine to craft new features to make sense of deeper layers of data
- Explore logistic regression, convolutional neural networks, recurrent neural networks, high level interfaces, & more
In this course, you’ll examine in detail the R programming language, the most popular statistical programming language in the world today. You’ll start by exploring different learning methods, clustering, classification, model evaluation methods and performance metrics. As you progress to more advanced subjects, you’ll develop the skills necessary to perform a variety of tasks with R.
- Access 35 lectures & 4 hours of content 24/7
- Delve into the general structure of clustering algorithms
- Develop applications in the R environment by using clustering & classification algorithms for real-life problems
- Use general definitions about artificial neural networks
- Explore the elements of deep learning neural networks & other types of deep learning networks
- Dive into developing machine learning algorithms w/ SparkR
You’ve seen deep learning everywhere, but you may not have realized it. This discipline is one of the leading solutions for image recognition, speech recognition, object recognition, and language translation – basically the tools you see Google roll out every day. Over this course, you’ll use Python to expand your deep learning knowledge to cover backpropagation and its ability to train neural networks.
- Access 19 lectures & 2 hours of content 24/7
- Train neural networks in deep learning & to understand automatic differentiation
- Cover convolutional & recurrent neural networks
- Build up the theory that covers supervised learning
- Integrate search & image recognition, & object processing
- Examine the performance of the sentimental analysis model
With increasing interest in AI around the world, deep learning has attracted a great deal of public attention. Every day, deep learning algorithms are used broadly across different industries. This book will give you a practical introduction to AI, including best practices using real-world use cases. You’ll learn to recognize and extract information to increase predictive accuracy and optimize results.
- Access 406 pages of digital content 24/7
- Get a practical deep dive into deep learning algorithms
- Explore deep learning further with Theano, Caffe, Keras, & TensorFlow
- Learn about two of the most powerful techniques at the core of many practical deep learning implementations: Auto-Encoders & Restricted Boltzmann Machines
- Dive into Deep Belief Nets & Deep Neural Networks
- Discover more deep learning algorithms with Dropout & Convolutional Neural Networks
- Get to know device strategies so you can use deep learning algorithms & libraries in the real world
This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer peceptron, and more sophisticated deep convolutional networks. You’ll also explore image processing, Recurrent Networks, and unsupervised learning algorithms such as Autoencoders. Finally, you’ll take a look at Reinforcement Learning and its application to AI game playing, another popular direction of research and application of neural networks.
- Access 318 pages of digital content 24/7
- Optimize step-by-step functions on a large neural network using the Backpropagation Algorithm
- Fine tune a neural network to improve the quality of results
- Use deep learning for image & audio processing
- Utilize Recursive Neural Tensor Networks (RNTNs) to outperform standard word embedding in special cases
- Identify problems for which Recurrent Neural Network (RNN) solutions are suitable
- Explore the process required to implement Autoencoders
- Evolve a deep neural network using reinforcement learning
This book will teach you how to deploy large-scale datasets in deep neural networks with Hadoop for optimal performance. Starting with an introduction to deep learning, you’ll learn how to set up a Hadoop envrionment and implement a variety of deep learning models. By the end of the book, you’ll know how to deploy various deep neural networks in distributed systems using Hadoop.
- Access 206 pages of digital content 24/7
- Explore deep learning & various models associated w/ it
- Understand the challenges of implementing distributed deep learning w/ Hadoop & how to overcome it
- Implement Convolutional Neural Network (CNN) w/ deeplearning4j
- Delve into the implementation of Restricted Boltzmann Machines (RBM)
- Understand the mathematical explanation for implement Recurrent Neural Networks (RNN)
- Get hands on practice w/ deep learning with Hadoop
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