Artificial Intelligence with Python - Deep Neural Networks
Artificial Intelligence with Python - Deep Neural Networks
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 1 Hour 19M | 338 MB
Genre: eLearning | Language: English


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Building Neural Networks with CNTK Using a Data Science Virtual Machine
Building Neural Networks with CNTK Using a Data Science Virtual Machine
MP4 | Video: AVC 1920x1080 | Audio: AAC 48KHz 2ch | Duration: 1 Hour | 511 MB
Genre: eLearning | Language: English


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TensorFlow for Neural Network Solutions

TensorFlow for Neural Network Solutions
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 1.5 Hours | 346 MB
Genre: eLearning | Language: English

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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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