The World Canine Organization (FCI) is currently listing more than 300 officially recognised dog breeds. Over thousands of years, mankind has managed to create an impressive diversity of canine phenotypes and an almost uncanny range of physical and behavioural characteristics of their faithful four-legged friends. However, apart from cynology scholars, dog breeders and some proven dog lovers most people shrug their shoulders in a clueless gesture, when asked to name the breed of a randomly presented dog, at least when it is not exactly a representative of one of the most popular and well known breeds like Dachshund, German Shepard or pug. If you are one of the few people who finds it slightly embarrassing not being able to identify dogs like a cynologist, you are probably pleased to learn that there might be a technical solution. Because thankfully, the aspiring and astonishing field of Deep Learning and artificial neural networks provides powerful concepts and methods for addressing this sort of classification tasks.
In this project we will develop ideas for a dog identification app using deep learning concepts. The software is intended to accept any user-supplied image as input. If a dog is detected in the image, it will provide an estimate of the dog’s breed. If a human is detected, it will provide an estimate of the dog breed that is most resembling.
Our project involves the following steps which will be covered in detail in the subsequent sections of this blog post.
#deep-learning #convolutional-network #image-recognition #tensorflow #data-science
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#deep learning development #deep learning framework #deep learning expert #deep learning ai #deep learning services
The Deep Learning DevCon 2020, DLDC 2020, has exciting talks and sessions around the latest developments in the field of deep learning, that will not only be interesting for professionals of this field but also for the enthusiasts who are willing to make a career in the field of deep learning. The two-day conference scheduled for 29th and 30th October will host paper presentations, tech talks, workshops that will uncover some interesting developments as well as the latest research and advancement of this area. Further to this, with deep learning gaining massive traction, this conference will highlight some fascinating use cases across the world.
Here are ten interesting talks and sessions of DLDC 2020 that one should definitely attend:
By Dipanjan Sarkar
**About: **Adversarial Robustness in Deep Learning is a session presented by Dipanjan Sarkar, a Data Science Lead at Applied Materials, as well as a Google Developer Expert in Machine Learning. In this session, he will focus on the adversarial robustness in the field of deep learning, where he talks about its importance, different types of adversarial attacks, and will showcase some ways to train the neural networks with adversarial realisation. Considering abstract deep learning has brought us tremendous achievements in the fields of computer vision and natural language processing, this talk will be really interesting for people working in this area. With this session, the attendees will have a comprehensive understanding of adversarial perturbations in the field of deep learning and ways to deal with them with common recipes.
By Divye Singh
**About: **Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER is a paper presentation by Divye Singh, who has a masters in technology degree in Mathematical Modeling and Simulation and has the interest to research in the field of artificial intelligence, learning-based systems, machine learning, etc. In this paper presentation, he will talk about the common problem of class imbalance in medical diagnosis and anomaly detection, and how the problem can be solved with a deep learning framework. The talk focuses on the paper, where he has proposed a synergistic over-sampling method generating informative synthetic minority class data by filtering the noise from the over-sampled examples. Further, he will also showcase the experimental results on several real-life imbalanced datasets to prove the effectiveness of the proposed method for binary classification problems.
By Dongsuk Hong
About: This is a paper presentation given by Dongsuk Hong, who is a PhD in Computer Science, and works in the big data centre of Korea Credit Information Services. This talk will introduce the attendees with machine learning and deep learning models for predicting self-employment default rates using credit information. He will talk about the study, where the DNN model is implemented for two purposes — a sub-model for the selection of credit information variables; and works for cascading to the final model that predicts default rates. Hong’s main research area is data analysis of credit information, where she is particularly interested in evaluating the performance of prediction models based on machine learning and deep learning. This talk will be interesting for the deep learning practitioners who are willing to make a career in this field.
#opinions #attend dldc 2020 #deep learning #deep learning sessions #deep learning talks #dldc 2020 #top deep learning sessions at dldc 2020 #top deep learning talks at dldc 2020
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According to dogtime.com, there are 266 different breeds of dogs, and by alone thinking about this number, it frightens me to distinguish them. And most of the people, if they’re normal, just know about 5–10 breeds because you don’t see the chapter “266 Different Dog Breeds” in a Bachelor’s Curriculum.
The main aim of this project is to build an algorithm to classify the different Dog Breeds from the dataset.
This seems like a simple task but when we think of Machine Learning, then it is not! The Images are in random order, having dogs at random spaces in the images, the images are shot in different lightenings, there is no preprocessing done on the data, it’s just a dataset containing simple dogs pictures.
So, the first step is to give the dataset a look.
The Dataset used for this project is Stanford Dogs Dataset. The Dataset contains a total of 20,580 images of 120 different dog breeds.
The Stanford Dogs dataset contains images of 120 breeds of dogs from around the world. This dataset has been built using images and annotation from ImageNet for the task of fine-grained image categorization.
import os import sys import keras import tarfile import numpy as np import tensorflow as tf import matplotlib.pyplot as plt from keras.models import Sequential from keras.engine.training import Model from sklearn.preprocessing import LabelBinarizer from keras.preprocessing.image import ImageDataGenerator from keras.layers import Add, Dropout, Flatten, Dense, Activation
I found 5 directories to be unusable and hence, didn’t used them. So, I imported a total of 115 Breeds.
import cv2 BASEPATH = './Images' LABELS = set() paths =  for d in os.listdir(BASEPATH): LABELS.add(d) paths.append((BASEPATH + '/' + d, d)) ## resizing and converting to RGB def load_and_preprocess_image(path): image = cv2.imread(path) image = cv2.resize(image, (224, 224)) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) return image X, y = ,  i = 0 for path, label in paths: i += 1 ## Faulty Directories if i == 18 or i == 23 or i == 41 or i == 49 or i == 90: continue if path == "./Images/.DS_Store": continue for image_path in os.listdir(path): image = load_and_preprocess_image(path + "/" + image_path) X.append(image) y.append(label)
Now, the names of the folder are in this pattern ‘n8725563753-Husky’, hence, we need to clean this up to be left with the _‘Husky’ _part of the name.
Y =  ## Cleaning the names of the directories/targets for i in y: Y.append(i.split('-'))
#transfer-learning #machine-learning #classification #deep-learning #convolutional-network
In the previous blog, we looked into the fact why Few Shot Learning is essential and what are the applications of it. In this article, I will be explaining the Relation Network for Few-Shot Classification (especially for image classification) in the simplest way possible. Moreover, I will be analyzing the Relation Network in terms of:
Moreover, effectiveness will be evaluated on the accuracy, time required for training, and the number of required training parameters.
Please watch the GitHub repository to check out the implementations and keep updated with further experiments.
In few shot classification, our objective is to design a method which can identify any object images by analyzing few sample images of the same class. Let’s the take one example to understand this. Suppose Bob has a client project to design a 5 class classifier, where 5 classes can be anything and these 5 classes can even change with time. As discussed in previous blog, collecting the huge amount of data is very tedious task. Hence, in such cases, Bob will rely upon few shot classification methods where his client can give few set of example images for each classes and after that his system can perform classification young these examples with or without the need of additional training.
In general, in few shot classification four terminologies (N way, K shot, support set, and query set) are used.
At this point, someone new to this concept will have doubt regarding the need of support and query set. So, let’s understand it intuitively. Whenever humans sees any object for the first time, we get the rough idea about that object. Now, in future if we see the same object second time then we will compare it with the image stored in memory from the when we see it for the first time. This applied to all of our surroundings things whether we see, read, or hear. Similarly, to recognise new images from query set, we will provide our model a set of examples i.e., support set to compare.
And this is the basic concept behind Relation Network as well. In next sections, I will be giving the rough idea behind Relation Network and I will be performing different experiments on 102-flower dataset.
The Core idea behind Relation Network is to learn the generalized image representations for each classes using support set such that we can compare lower dimensional representation of query images with each of the class representations. And based on this comparison decide the class of each query images. Relation Network has two modules which allows us to perform above two tasks:
We can define the whole procedure in just 5 steps.
Few things to know during the training is that we will use only images from the set of selective class, and during the testing, we will be using images from unseen classes. For example, from the 102-flower dataset, we will use 50% classes for training, and rest will be used for validation and testing. Moreover, in each episode, we will randomly select 5 classes to create the support and query set and follow the above 5 steps.
That is all need to know about the implementation point of view. Although the whole process is simple and easy to understand, I’ll recommend reading the published research paper, Learning to Compare: Relation Network for Few-Shot Learning, for better understanding.
#deep-learning #few-shot-learning #computer-vision #machine-learning #deep learning #deep learning
This is the third part of a series of posts showing the improvements in NLP modeling approaches. We have seen the use of traditional techniques like Bag of Words, TF-IDF, then moved on to RNNs and LSTMs. This time we’ll look into one of the pivotal shifts in approaching NLP Tasks — Transfer Learning!
The complete code for this tutorial is available at this Kaggle Kernel
The idea of using Transfer Learning is quite new in NLP Tasks, while it has been quite prominently used in Computer Vision tasks! This new way of looking at NLP was first proposed by Howard Jeremy, and has transformed the way we looked at data previously!
The core idea is two-fold — using generative pre-trained Language Model + task-specific fine-tuning was first explored in ULMFiT (Howard & Ruder, 2018), directly motivated by the success of using ImageNet pre-training for computer vision tasks. The base model is AWD-LSTM.
A Language Model is exactly like it sounds — the output of this model is to predict the next word of a sentence. The goal is to have a model that can understand the semantics, grammar, and unique structure of a language.
ULMFit follows three steps to achieve good transfer learning results on downstream language classification tasks:
fast.ai’s motto — Making Neural Networks Uncool again — tells you a lot about their approach ;) Implementation of these models is remarkably simple and intuitive, and with good documentation, you can easily find a solution if you get stuck anywhere. Along with this, and a few other reasons I elaborate below, I decided to try out the fast.ai library which is built on top of PyTorch instead of Keras. Despite being used to working in Keras, I didn’t find it difficult to navigate fast.ai and the learning curve is quite fast to implement advanced things as well!
In addition to its simplicity, there are some advantages of using fast.ai’s implementation -
Weight update for Stochastic Gradient Descent (SGD). ∇θ(ℓ)J(θ) is the gradient of Loss Function with respect to θ(ℓ). η(ℓ) is the learning rate of the ℓ-th layer.
#nlp #machine-learning #transfer-learning #deep-learning #sentiment-classification #deep learning