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At every point in time during training, a neural network has a certain loss, or error, calculated using a cost function (also referred to as a loss function). This function indicates how ‘wrong’ the network (parameters) is based on the training or validation data. Optimally, the loss would be as low as possible. Unfortunately, cost functions are nonconvex — they don’t just have one minimum, but many, many local minima.

Continue reading An Empirical Comparison of Optimizers for Machine Learning Models

Humans have been trying to augment their reality since the 1990s, when Boeing developed their own AR for assembling their airplanes. The technology gained popularity with the launch of the first version of Google Glass. However, AR has been widely adopted in the consumer market only in the past 5 years, as handheld devices have become powerful enough to host such experiences.

Continue reading Augmented Reality (AR) Development: Tools and Platforms

This is an implementation using the concepts of Q-Learning, which I covered in a previous blog post providing a high-level overview of reinforcement learning (RL).

To help demonstrate these concepts, I’ll be covering how to automate an agent to find the shortest route from its source to a particular destination, recognizing the environment and obstacles, thus learning from its experiences. A typical example would be a robot exploring the environment and finding the optimal way to the goal.

Continue reading Automating an AI to find shortest route using reinforcement learning

Fritz Vision includes a comprehensive API to process pre-recorded video. Some of the things we can do with it include running predictions on every frame, exporting the video as well as extracting individual frames. Fritz Vision also has the power to change old experiences into new ones using machine learning augmentations. In this article, we will see how we can use Fritz to apply filters and perform segmentation of people in a video. This is the result of such an operation.

Continue reading Video Processing in Android with Fritz AI

In a naive encoder-decoder model, one RNN unit reads a sentence, and the other one outputs a sentence, as in machine translation.

But what can be done to improve this model’s performance? Here, we’ll explore a modification to this encoder-decoder mechanism, commonly known as an attention model.

In machine translation, we’re feeding our input into the encoder (green part) of the network, with the output coming from the decoder (purple part) of the network, as depicted above.

Continue reading Attention Model in an Encoder-Decoder

🔋 Low-power consumption is indispensable for autonomous/unmanned vehicles and IoT (Internet of Things) devices and appliances. In order to develop deep learning inference applications at the edge, we can use Intel’s energy-efficient and low-cost Movidius USB stick!

💎 The Movidius Neural Compute Stick (NCS) is produced by Intel and can be run without an Internet connection. The Movidius NCS’ compute capability comes from its Myriad 2 VPU (Vision Processing Unit).

Continue reading A Brief Guide to the Intel Movidius Neural Compute Stick with Raspberry Pi 3

Support vector regression (SVR) is a statistical method that examines the linear relationship between two continuous variables.

In regression problems, we generally try to find a line that best fits the data provided. The equation of the line in its simplest form is described as below y=mx +c

In the case of regression using a support vector machine, we do something similar but with a slight change. Here we define a small error value e (error = prediction – actual).

Continue reading Support Vector Regression in Python Using Scikit-Learn

According to Statistica, 25.6 billion units of microcontrollers were shipped in 2019. There are over 250 billion microcontrollers in the world and this number is projected to grow over the coming years. As a result of this, deep learning on embedded devices is one of the fastest-growing fields.

Continue reading TensorFlow Lite Micro

With fast-paced advances in neural network architecture, deep and machine learning research, and ever-increasing hardware + software resources, the number of incredible demo projects seems to increase at a near-dizzying rate.

From AI-generated art and enhanced accessibility, to tracking human movement in real-time, and beyond, we’ve curated some of our favorite deep learning projects with accompanying visual demos.

Continue reading Best Machine Learning Projects — with Visual Demos

To kick things off with this tutorial on how to build you own recommender systems in Python, we’ll learn how to make an e-commerce item recommender system with a technique called content-based filtering.

Unfortunately, as of the day of this post’s publication, Wikipedia defines recommender systems too narrowly, as “a subclass of information filtering systems that seeks to predict the ‘rating’ or ‘preference’ that a user would give to an item”. Recommender systems are much more than this definition.

Continue reading Recommender Systems with Python: Content-Based Filtering