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Neural Networks and Convolutional Neural Networks Essential Training
Neural Networks and Convolutional Neural Networks Essential Training
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 1 Hour 19M | 225 MB
Genre: eLearning | Language: English


Take a deep dive into neural networks and convolutional neural networks, two key concepts in the area of machine learning. In this hands-on course, instructor Jonathan Fernandes covers fundamental neural and convolutional neural network concepts. Jonathan begins by providing an introduction to the components of neural networks, discussing activation functions and backpropagation. He then looks at convolutional neural networks, explaining why they're particularly good at image recognition tasks. He also steps through how to build a neural network model using Keras. Plus, learn about VGG16, the history of the ImageNet challenge, and more.
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Building Neural Networks with CNTK Using Apache Spark and Azure HDInsight
Building Neural Networks with CNTK Using Apache Spark and Azure HDInsight
.MP4, AVC, 461 kbps, 1920x1080 | English, AAC, 235 kbps, 2 Ch | 41 mins | 210 MB
Instructor: Mark Tabladillo


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Artificial Intelligence Foundations Neural Networks

Artificial Intelligence Foundations: Neural Networks
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 1 Hour 16M | 458 MB
Genre: eLearning | Language: English

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Advanced Neural Networks with Tensorflow

Advanced Neural Networks with Tensorflow
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 3.5 Hours | 534 MB
Genre: eLearning | Language: English

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Neural Networks in Machine Learning for Developers
Neural Networks in Machine Learning for Developers
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 44M | 157 MB
Genre: eLearning | Language: English

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Building Neural Networks with MXNet
Building Neural Networks with MXNet
.MP4, AVC, 780 kbps, 1920x1080 | English, AAC, 235 kbps, 2 Ch | 1 hr 12 mins | 525 MB
Instructor: Mark Tabladillo

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CS231n: Convolutional Neural Networks (Deep Learning)
English | Size: 4.75 GB
Category: tUTORIAL

Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. During the 10-week course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. The final assignment will involve training a multi-million parameter convolutional neural network and applying it on the largest image classification dataset (ImageNet). We will focus on teaching how to set up the problem of image recognition, the learning algorithms (e.g. backpropagation), practical engineering tricks for training and fine-tuning the networks and guide the students through hands-on assignments and a final course project. Much of the background and materials of this course will be drawn from the ImageNet Challenge.
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Building Neural Networks with TensorFlow
Building Neural Networks with TensorFlow
MP4 | Video: AVC 1920x1080 | Audio: AAC 48KHz 2ch | Duration: 1 Hour | 378 MB
Genre: eLearning | Language: English

TensorFlow is an open-source library whose primary purpose is to help developers and data scientists build deep neural networks. Learn the basics of TensorFlow and see it in action as a first step in building deep neural networks of your own.

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Sentiment Analysis with Recurrent Neural Networks in TensorFlow
Sentiment Analysis with Recurrent Neural Networks in TensorFlow
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 3 Hours | 520 MB
Genre: eLearning | Language: English

Recurrent neural networks (RNNs) are ideal for considering sequences of data. In this course, you'll explore how word embeddings are used for sentiment analysis using neural networks.
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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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