Neural Networks: Instructional Materials

How to Speed Up Deep Learning Inference Using TensorRT NVIDIA Developer Blog


Welcome to this introduction to TensorRT, our platform for deep learning inference. You will learn how to deploy a deep learning application onto a GPU, increasing throughput and reducing latency during inference. TensorRT provides APIs and parsers to import trained models from all major deep learning frameworks. It then generates optimized runtime engines deployable in the datacenter as well as in automotive and embedded environments. Applications deployed on GPUs with TensorRT perform up to 40x faster than CPU-only platforms. This tutorial uses a C example to walk you through importing an ONNX model into TensorRT, applying optimizations, and generating a high-performance runtime engine for the datacenter environment.

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We hope you have your holiday shopping game face on because the details today are pretty excellent. We're rounding up the best deals from Amazon, Walmart, Best Buy, and Macy's on Apple products, laptops and accessories, kitchen appliances, and even Amazon's own devices. We're also highlighting deals on Udemy online classes in case you feel inspired to learn a little something. There are a number of Apple products on sale, such as the Apple MacBook (mid-2017) 12-inch laptop, which is priced at $999.99, or $400 off its list price. It seems that the previous generation is on sale now before the new MacBook Air 2018 model hits store shelves .

A Gentle Introduction to LSTM Autoencoders


An LSTM Autoencoder is an implementation of an autoencoder for sequence data using an Encoder-Decoder LSTM architecture. Once fit, the encoder part of the model can be used to encode or compress sequence data that in turn may be used in data visualizations or as a feature vector input to a supervised learning model. In this post, you will discover the LSTM Autoencoder model and how to implement it in Python using Keras. A Gentle Introduction to LSTM Autoencoders Photo by Ken Lund, some rights reserved. An autoencoder is a neural network model that seeks to learn a compressed representation of an input.



In the fall of 2016, I was a Teaching Fellow (Harvard's version of TA) for the graduate class on "Advanced Topics in Data Science (CS209/109)" at Harvard University. I was in-charge of designing the class project given to the students, and this tutorial has been built on top of the project I designed for the class. As a researcher on Computer Vision, I come across new blogs and tutorials on ML (Machine Learning) every day. However, most of them are just focussing on introducing the syntax and the terminology relevant to the field. While people are able to copy paste and run the code in these tutorials and feel that working in ML is really not that hard, it doesn't help them at all in using ML for their own purposes.

TensorFlow: Building Feed-Forward Neural Networks Step-by-Step


In this article, two basic feed-forward neural networks (FFNNs) will be created using TensorFlow deep learning library in Python. The reader should have basic understanding of how neural networks work and its concepts in order to apply them programmatically. This article will take you through all steps required to build a simple feed-forward neural network in TensorFlow by explaining each step in details. Before actual building of the neural network, some preliminary steps are recommended to be discussed. Here is the first classification problem that we are to solve using neural network.

PyTorch Scholarship Challenge from Facebook Udacity


During the first phase of this program, students take Udacity's "Introduction to Deep Learning with PyTorch" course. The duration of this course is two months. Program participants will receive support from community managers throughout their learning experience in this course, and will be part of a dynamic student community and network of scholars. The top 300 students from the first phase of the program will earn a full scholarship to Udacity's Deep Learning Nanodegree program, where they'll cover Convolutional and Recurrent Neural Networks, Generative Adversarial Networks, Deployment, and more. Students will use PyTorch, and have access to GPUs to train models faster, as they learn from authorities like Sebastian Thrun, Ian Goodfellow, Jun-Yan Zhu, and Andrew Trask.

Machine Learning Fun and Easy - YouTube


Welcome to the Fun and Easy Machine learning Course in Python and Keras. Are you Intrigued by the field of Machine Learning? Then this course is for you! We will take you on an adventure into the amazing of field Machine Learning. Each section consists of fun and intriguing white board explanations with regards to important concepts in Machine learning as well as practical python labs which you will enhance your comprehension of this vast yet lucrative sub-field of Data Science.

Introduction to Neural Networks, Deep Learning ( course)


Having a solid grasp on deep learning techniques feels like acquiring a super power these days. From classifying images and translating languages to building a self-driving car, all these tasks are being driven by computers rather than manual human effort. Deep learning has penetrated into multiple and diverse industries, and it continues to break new ground on an almost weekly basis. Understandably, a ton of folks are suddenly interested in getting into this field. But where should you start? What are the core concepts that actually make up this complex yet intriguing field? I'm excited to pen down a series of articles where I will break down the basic components that every deep learning enthusiast should know thoroughly. My inspiration comes from, In this article, I will be writing about Course 1 of the specialization, where the great Andrew Ng explains the basics of Neural Networks and how to implement them. Note: We will follow a bottom-up approach throughout this series – we will first understand the concept from the ground-up, and only then follow it's implementation.

Free Online Course: Neural Networks for Machine Learning from Coursera Class Central


I honestly can't understand the multiple 5 star reviews presented on this site about the course. I'm giving it a 1 star which is a bit harsh I know but I'm doing it to offset the number of 5 star reviews here. Honestly I think the course deserves something between 2 and 3 stars depending on your approach to it. Yes Prof. Hinton is a leading expert in the field but the course materials and the way they are presented are pretty bad! I honestly can't understand the multiple 5 star reviews presented on this site about the course.