Deep learning models rely on numerical vectors to'understand' the input words. We can think of the numerical vectors as high dimensional features representing the input words. In this high dimensional space, words are located close together or far away from each other. Word representation is built by finding the proper numerical vector representations for all the words in a given corpus. The quality of word representation relies on the corpus. This can be easily understood in the way that two human beings can have a different understanding of the same word, depending on whether he likes to spend time reading the modern newspaper or Shakespeare's literature. Besides, the quality of word representation heavily relies on the methods to find numerical vector representations for all the words. There are several methods to generate word representation by learning from the words' context.

Deep neural networks have been responsible for much of the advances in machine learning over the last decade. These restrictions not only raise infrastructure costs but also complicate network implementation in resource-constrained contexts like mobile phones and smart devices. Neural network pruning, which comprises methodically eliminating parameters from an existing network, is a popular approach for minimizing the resource requirements at test time. The goal of neural network pruning is to convert a large network to a smaller network with equivalent accuracy. Here in this article, we will discuss the important points related to neural network pruning. The major points to be covered in this article are listed below.

One of the highlights of my technical life in 2020 was getting access to GPT-3. Thanks to the advice provided by in this video, I was able to get access to GPT-3 and publish a series of videos describing experiments I did with GPT-3 to generate git commands from English, to create a movie trivia chatbot, and to navigate the London Underground and the New York City subway. I was impressed with the variety of problems that GPT-3 could tackle, so when I heard about Codex I was anxious to try it out. Codex focused on one of the capabilities of GPT-3, generating code from English language descriptions. Codex generates code in a variety of languages, including Python and JavaScript.

There are different modules to realize different functions in deep learning. Expertise in deep learning involves designing architectures to complete particular tasks. It reduces a complex function into a graph of functional modules (possibly dynamic), the functions of which are finalized by learning. Recurrent Neural Network (RNN)is one type of architecture that we can use to deal with sequences of data. We learned that a signal can be either 1D, 2D or 3D depending on the domain. The domain is defined by what you are mapping from and what you are mapping to.

TrackMate is an automated tracking software used to analyze bioimages and distributed as a Fiji plugin. Here we introduce a new version of TrackMate rewritten to improve performance and usability, and integrating several popular machine and deep learning algorithms to improve versatility. We illustrate how these new components can be used to efficiently track objects from brightfield and fluorescence microscopy images across a wide range of bio-imaging experiments. Object tracking is an essential image analysis technique used across biosciences to quantify dynamic processes. In life sciences, tracking is used for instance to track single particles, sub-cellular organelles, bacteria, cells, and whole animals.

Artificial intelligence (AI) is not a new kid on the block anymore and the field is developing at a constantly increasing pace. Pretty much every day there is some kind of new development, be it a research paper announcing a new or improved machine learning algorithm, a new library for one of the most popular programming languages (Python/R/Julia), etc. In the past, many of those advances did not make it to mainstream media. But that is also changing rapidly. Some of the recent examples include the AlphaGo beating the 18-time world champion at Go [1], using Deep Learning to generate realistic faces of humans that never existed [2], or the spread of Deep Fakes -- images or videos placing people in situations that never actually happened.

I was given Xray baggage scan images by an airport to develop a model that performs automatic detection of dangerous objects (gun and knife). Given only a small amount of Xray images, I am using Domain Adaptation by first collecting a large number of normal (non-Xray) images of dangerous objects from the internet, training a model using only those normal images, then adapting the model to perform well on Xray images. In my previous post, I talked about iterative data collection process for web images of gun and knife to be used for domain adaptation. In this post, I will discuss transfer learning with ResNet50 using the scraped web images. For now, we won't worry about the Xray images and only focus on training the model with the web images. To read this post, it's recommended to have some knowledge about how to apply transfer learning using a model pre-trained on ImageNet in PyTorch. I won't explain every step in detail, but will share some useful tips that can answer questions like:

The use of deep learning models is becoming more and more democratic every day and is becoming indispensable in many industries. Nevertheless, the implementation of efficient neural networks generally requires a background in architectural engineering and lots of time to explore in an iterative process the full range of solutions to our knowledge. The form and architecture of a neural network will vary in its use for a specific need. It is therefore necessary to design an architecture-specific to the given need. Designing these networks in a trial-and-error way is then a tedious task and requires architectural engineering skills and domain expertise.

Classify the logos(among 6 selected brands) using deep learning:. This Model Predicts whether the image is of which brand by seeing logos using Convolution Neural Network..

A deep clustering network is desired for data streams because of its aptitude in extracting natural features thus bypassing the laborious feature engineering step. While automatic construction of the deep networks in streaming environments remains an open issue, it is also hindered by the expensive labeling cost of data streams rendering the increasing demand for unsupervised approaches. This paper presents an unsupervised approach of deep clustering network construction on the fly via simultaneous deep learning and clustering termed Autonomous Deep Clustering Network (ADCN). It combines the feature extraction layer and autonomous fully connected layer in which both network width and depth are self-evolved from data streams based on the bias-variance decomposition of reconstruction loss. The self-clustering mechanism is performed in the deep embedding space of every fully connected layer while the final output is inferred via the summation of cluster prediction score. Further, a latent-based regularization is incorporated to resolve the catastrophic forgetting issue. A rigorous numerical study has shown that ADCN produces better performance compared to its counterparts while offering fully autonomous construction of ADCN structure in streaming environments with the absence of any labeled samples for model updates. To support the reproducible research initiative, codes, supplementary material, and raw results of ADCN are made available in \url{https://tinyurl.com/AutonomousDCN}.