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Deep Learning A-Z Hands-On Artificial Neural Networks

Deep Learning A-Z Hands-On Artificial Neural Networks
English | Size: 3.15 GB
Category: CBTs

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Category: Tutorial
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Packt Publishing - Learning Neural Networks with Tensorflow
English | Size: 725.19 MB
Category: CBTs

The video is packed with step-by-step instructions, working examples, and helpful advice about building your Neural Network with Tensorflow. You'll learn to build your own network. This practical course is divided into clear byte-size chunks so you can learn at your own pace and focus on the areas of most interest to you.

Neural Networks are used all around us: they index photos into categories, translate text, suggest replies for emails, and beat the best games. Many people are eager to apply this knowledge to their own data, but many fail to achieve the results they expect.

In this course, we’ll start by building a simple flower recognition program, making you feel comfortable with Tensorflow, and it will teach you several important concepts in Neural Networks. Next, you’ll start working with high-dimensional uses to predict one output: 1275 molecular features you can use to predict the atomization energy of an atom. The next program we’ll create is a handwritten number recognition system trained on the famous MNIST dataset. We’ll work our way up from a simple multilayer perceptron to a state of the art Deep Convolutional Neural Network.
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Category: Tutorial
Learning Neural Networks with Tensorflow
Learning Neural Networks with Tensorflow
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 3.5 Hours | 634 MB
Genre: eLearning | Language: English

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Category: Tutorial
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Live Lessons - Deep Learning for Natural Language Processing Applications of Deep Neural Networks to Machine Learning Tasks
English | Size: 8.59 GB
Category: CBTs

An intuitive introduction to processing natural language data with Deep Learning models Deep Learning for Natural Language Processing LiveLessons is an introduction to processing natural language with Deep Learning. These lessons bring intuitive explanations of essential theory to life with interactive, hands-on Jupyter notebook demos. Examples feature Python and Keras, the high-level API for TensorFlow, the most popular Deep Learning library. In the early lessons, specifics of working with natural language data are covered, including how to convert natural language into numerical representations that can be readily processed by machine learning approaches. In the later lessons, state-of-the art Deep Learning architectures are leveraged to make predictions with natural language data.
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Category: Tutorial
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Deep Learning A-Z™ Hands-On Artificial Neural Networks
English | Size: 3.15 GB
Category: CBTs

Artificial intelligence is growing exponentially. There is no doubt about that. Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors and Google Deepmind's AlphaGo beat the World champion at Go - a game where intuition plays a key role.

But the further AI advances, the more complex become the problems it needs to solve. And only Deep Learning can solve such complex problems and that's why it's at the heart of Artificial intelligence.
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Category: Tutorial

Python and TensorFlow Applied to Deep Learning and Neural Networks

Python and TensorFlow Applied to Deep Learning and Neural Networks
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 40M | 257 MB
Genre: eLearning | Language: English

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Category: Tutorial
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Coursera - Neural Networks and Deep Learning by Andrew Ng
English | Size: 609.04 MB
Category: Tutorial

If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "superpower" that will let you build AI systems that just weren't possible a few years ago.

In this course, you will learn the foundations of deep learning. When you finish this class, you will:
- Understand the major technology trends driving Deep Learning
- Be able to build, train and apply fully connected deep neural networks
- Know how to implement efficient (vectorized) neural networks
- Understand the key parameters in a neural network's architecture
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Category: E-Book
MATLAB Deep Learning: With Machine Learning, Neural Networks and Artificial Intelligence
MATLAB Deep Learning: With Machine Learning, Neural Networks and Artificial Intelligence
English | 15 Jun. 2017 | ISBN: 1484228448 | 172 Pages | EPUB | 1.91 MB
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Category: Tutorial
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O'Reilly - Neural Networks
English | Size: 229.32 MB
Category: Tutorial

Neural networks form the foundation for deep learning, the most advanced and popular machine learning technique in use today. This course provides an introduction to neural networks. It begins with an overview of a neural network's basic concepts and building blocks - neurons, weights, activations, and layers - before explaining how to train one using gradient descent. The optimization technique is explained with a visual example and different issues such as parameter initialization and model validation are discussed. The course covers the different types of neural network architectures, explains the differences between them, and illustrates practical applications for each. Because training a neural network can be very slow, the course will offer up some tricks for speeding up the process and improving results. The course ends with a review of the history of this fascinating field, from its origin to its fall, and then its subsequent rise in modern days. Requirements include a clear understanding of supervised learning and optimization.
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Category: E-Book
Advances in Neural Networks - ISNN 2016: 13th International Symposium on Neural Networks

Advances in Neural Networks - ISNN 2016: 13th International Symposium on Neural Networks, ISNN 2016, St. Petersburg, Russia, July 6-8, 2016, Proceedings by Long Cheng, Qingshan Liu, Andrey Ronzhin
English | 2016 | ISBN: 3319406620 | 741 Pages | PDF | 63.5 MB
This book constitutes the refereed proceedings of the 13th International Symposium on Neural Networks, ISNN 2016, held in St. Petersburg, Russia in July 2016.
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