In today's blog post, I demonstrated how to train a simple neural network using Python and Keras. We then applied our neural network to the Kaggle Dogs vs. Cats dataset and obtained 67.376% accuracy utilizing only the raw pixel intensities of the images. Starting next week, I'll begin discussing optimization methods such as gradient descent and Stochastic Gradient Descent (SGD). I'll also include a tutorial on backpropagation to help you understand the inner-workings of this important algorithm.

Due to the recent achievements of artificial neural networks across many different tasks (such as face recognition, object detection and Go), deep learning has become extremely popular. This post aims to be a starting point for those interested in learning more about it. If you already have a basic understanding of linear algebra, calculus, probability and programming: I recommend starting with Stanford's CS231n. The course notes are comprehensive and well-written. The slides for each lesson are also available, and even though the accompanying videos were removed from the official site, re-uploads are quite easy to find online.

Machine learning is one of the fastest-growing and most exciting fields out there, and deep learning represents its true bleeding edge. In this course, you'll develop a clear understanding of the motivation for deep learning, and design intelligent systems that learn from complex and/or large-scale datasets. We'll show you how to train and optimize basic neural networks, convolutional neural networks, and long short term memory networks. Complete learning systems in TensorFlow will be introduced via projects and assignments. You will learn to solve new classes of problems that were once thought prohibitively challenging, and come to better appreciate the complex nature of human intelligence as you solve these same problems effortlessly using deep learning methods.

Numerical Optimization is one of the central techniques in Machine Learning. For many problems it is hard to figure out the best solution directly, but it is relatively easy to set up a loss function that measures how good a solution is - and then minimize the parameters of that function to find the solution. I ended up writing a bunch of numerical optimization routines back when I was first trying to learn javascript. Since I had all this code lying around anyway, I thought that it might be fun to provide some interactive visualizations of how these algorithms work. The cool thing about this post is that the code is all running in the browser, meaning you can interactively set hyper-parameters for each algorithm, change the initial location, and change what function is being called to get a better sense of how these algorithms work.

We've added some blogs that were missing in the original list, and eliminated some that aren't worth mentioning, hoping to make this list less biased. AnalyticBridge, about advanced analytics, books, salary surveys, training, challenges. Anil Batra's Web Analysis (Analytics), Online Advertising and Behavioral Targeting blog BigDataNews General articles about big data, as well as news (selected press releases) Business. CoolData By Kevin MacDonell on Analytics, predictive modeling and related cool data stuff for fund-raising in higher education. Cloud of data blog By Paul Miller, aims to help clients understand the implications of taking data and more to the Cloud.

Makes your study time more efficient by focusing on the topics you where need the most help. Proven to help students earn a better grade in their courses. Before You Buy: This is an online third party study guide to accompany Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data and is not meant for submitting homework assignments. This product does not accept a course key. If one was provided to you, this is not the correct product.

Traditional neural networks rely on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layer, or so-called more depth. These kind of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. TensorFlow is one of the best libraries to implement deep learning. TensorFlow is a software library for numerical computation of mathematical expressional, using data flow graphs.

The presidential campaign made clear that chauvinist attitudes toward women remain stubbornly fixed in some parts of society. It turns out we're inadvertently teaching artificial-intelligence systems to be sexist, too. New research shows that subtle gender bias is entrenched in the data sets used to teach language skills to AI programs. As these systems become more capable and widespread, their sexist point of view could have negative consequences--in job searches, for instance. The problem results from the way machines are being taught to read and talk.

Today's developers often hear about leveraging machine learning algorithms in order to build more intelligent applications, but many don't know where to start. One of the most important aspects of developing smart applications is to understand the underlying machine learning models, even if you aren't the person building them. Whether you are integrating a recommendation system into your app or building a chat bot, this guide will help you get started in understanding the basics of machine learning. This introduction to machine learning and list of resources is adapted from my October 2016 talk at ACT-W, a women's tech conference. Machine learning studies computer algorithms for learning to do stuff.

The Machine Learning Canvas is a template for developing new (or documenting existing) intelligent systems based on data and machine learning. It is a visual chart with elements describing the key aspects of such systems: the value proposition, the data to learn from (to create predictive models), the utilization of predictions (to create proposed value), requirements and measures of performance. It assists teams of data scientists, software engineers, product and business managers, in aligning their activities. This tutorial will help you get into the right mindset to go beyond the current hype around machine learning, beyond proofs of concept, and to clearly see how this technology can have an actual impact in your domain. I'll present the general structure of the Canvas, the different boxes it is composed of and the associated questions to answer. We'll see how to fill it in iteratively on a churn prevention example.