Is Deep Learning Necessary For Simple Classification Tasks

Deep learning (DL) models are known for tackling the nonlinearities associated with data, which the traditional estimators such as logistic regression couldn’t. However, there is still a cloud of doubt with regards to the increased use of computationally intensive DL for simple classification tasks. To find out if DL really outperforms shallow models significantly, the researchers from the University of Pennsylvania experiment with three ML pipelines that involve traditional methods, AutoML and DL in a paper titled, ‘Is Deep Learning Necessary For Simple Classification Tasks.’

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#deep-learning #automl #machine-learning

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Is Deep Learning Necessary For Simple Classification Tasks
Marget D

Marget D


Top Deep Learning Development Services | Hire Deep Learning Developer

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We at Inexture, strategically work on every project we are associated with. We propose a robust set of AI, ML, and DL consulting services. Our virtuoso team of data scientists and developers meticulously work on every project and add a personalized touch to it. Because we keep our clientele aware of everything being done associated with their project so there’s a sense of transparency being maintained. Leverage our services for your next AI project for end-to-end optimum services.

#deep learning development #deep learning framework #deep learning expert #deep learning ai #deep learning services

Mikel  Okuneva

Mikel Okuneva


Top 10 Deep Learning Sessions To Look Forward To At DVDC 2020

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:

Also Read: Why Deep Learning DevCon Comes At The Right Time

Adversarial Robustness in Deep Learning

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.

Read an interview with Dipanjan Sarkar.

Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER

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.

Default Rate Prediction Models for Self-Employment in Korea using Ridge, Random Forest & Deep Neural Network

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

Few Shot Learning — A Case Study (2)

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:

  1. Effectiveness of different architectures such as Residual and Inception Networks
  2. Effects of transfer learning via using pre-trained classifier on ImageNet dataset

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.

Introduction to Few-Shot Classification

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.

  1. N way: It means that there will be total N classes which we will be using for training/testing, like 5 classes in above example.
  2. K shot: Here, K means we have only K example images available for each classes during training/testing.
  3. Support set: It represents a collection of all available K examples images from each classes. Therefore, in support set we have total N*K images.
  4. Query set: This set will have all the images for which we want to predict the respective classes.

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.

About Relation Network

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:

  1. Embedding module: This module will extract the required underlying representations from each input images irrespective of the their classes.
  2. Relation Module: This module will score the relation of embedding of query image with each class embedding.

Training/Testing procedure:

We can define the whole procedure in just 5 steps.

  1. Use the support set and get underlying representations of each images using embedding module.
  2. Take the average of between each class images and get the single underlying representation for each class.
  3. Then get the embedding for each query images and concatenate them with each class’ embedding.
  4. Use the relation module to get the scores. And class with highest score will be the label of respective query image.
  5. [Only during training] Use MSE loss functions to train both (embedding + relation) modules.

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

Transfer Learning in Image Classification

The term Transfer Learning refers to the leverage of knowledge gained by a Neural Network trained on a certain (usually large) available dataset for solving new tasks for which few training examples are available, integrating the existing knowledge with the new one learned from the few examples of the task-specific dataset. Transfer Learning is thus commonly used, often together with other techniques such as Data Augmentation, in order to address the problem of lack of training data.

But, in practice, how much can Transfer Learning actually help, and how many training examples do we really need in order for it to be effective?

In this story, I try to answer these questions by applying two Transfer Learning techniques (e.g. Feature Extraction and Fine-Tuning) for addressing an Image Classification task, varying the number of examples on which the models are trained in order to see how the lack of data affects the effectiveness of the adopted approaches.

Experimental Case Study

The task chosen for experimenting Transfer Learning consists of the classification of flower images into 102 different categories. The choice of this task is mainly due to the easy availability of a flowers dataset, as well as to the domain of the problem, which is generic enough to be suitable for effectively applying Transfer Learning with neural networks pre-trained on the well-known ImageNet dataset.

The adopted dataset is the 102 Category Flower Dataset created by M. Nilsback and A. Zisserman [3], which is a collection of 8189 labelled flowers images belonging to 102 different classes. For each class, there are between 40 and 258 instances and all the dataset images have significant scale, pose and light variations. The detailed list of the 102 categories together with the respective number of instances is available here.

Figure 1: Examples of images extracted from the 102 Category Dataset.

In order to create training datasets of different sizes and evaluate how they affect the performance of the trained networks, the original set of flowers images is split into training, validation and test sets several times, each time adopting different split percentages. Specifically, three different training sets are created (that from now on will be referred to as the LargeMedium and Small training sets) using the percentages shown in the table below.

Table 1: number of examples and split percentages (referred to the complete unpartitioned flowers dataset) of the datasets used to perform the experiments.

All the splits are performed adopting stratified sampling, in order to avoid introducing sampling biases and ensuring in this way that all the obtained training, validation and test subsets are representative of the whole initial set of images.

Adopted strategies

The image classification task described above is addressed by adopting the two popular techniques that are commonly used when applying Transfer Learning with pre-trained CNNs, namely Feature Extraction and Fine-Tuning.

Feature Extraction

Feature Extraction basically consists of taking the convolutional base of a previously trained network, running the target data through it and training a new classifier on top of the output, as summarized in the figure below.

Figure 2: Feature Extraction applied to a convolutional neural network: the classifiers are swapped while the same convolutional base is kept. “Frozen” means that the weighs are not updated during training.

The classifier stacked on top of the convolutional base can either be a stack of fully-connected layers or just a single Global Pooling layer, both followed by Dense layer with softmax activation function. There is no specific rule regarding which kind of classifier should be adopted, but, as described by Lin et. al [2], using just a single Global Pooling layer generally leads to less overfitting since in this layer there are no parameters to optimize.

Consequently, since the training sets used in the experiments are relatively small, the chosen classifier only consists of a single Global Average Pooling layer which output is fed directly into a softmax activated layer that outputs the probabilities for each of the 102 flowers categories.

During the training, only the weights of the top classifiers are updated, while the weights of the convolutional base are “frozen” and thus kept unchanged.

In this way, the shallow classifier learns how to classify the flower images into the possible 102 categories from the off-the-shelf representations previously learned by the source model for its domain. If the source and the target domains are similar, then these representations are likely to be useful to the classifier and the transferred knowledge can thus bring an improvement to its performance once it is trained.


Fine-Tuning can be seen as a further step than Feature Extraction that consists of selectively retraining some of the top layers of the convolutional base previously used for extracting features. In this way, the more abstract representations of the source model learned by its last layers are slightly adjusted to make them more relevant for the target problem.

This can be achieved by unfreezing some of the top layers of the convolutional base, keeping frozen all its other layers and jointly training the convolutional base with the same classifier previously used for Feature Extraction, as represented in the figure below.

Figure 3: Feature Extraction compared to Fine-Tuning.

It is important to point out that, according to F. Chollet, the top layers of a pre-trained convolutional base can be fine-tuned only if the classifier on top of it has already been previously trained. The reason is that if the classifier was not already trained, then its weights would be randomly initialized. As a consequence, the error signal propagating through the network during training would be too large and the unfrozen weights would be updated disrupting the abstract representations previously learned by the convolutional base.

#deep-learning #machine-learning #artificial-intelligence #image-classification #transfer-learning #deep learning

Tia  Gottlieb

Tia Gottlieb


Deep Reinforcement Learning for Video Games Made Easy

In this post, we will investigate how easily we can train a Deep Q-Network (DQN) agent (Mnih et al., 2015) for Atari 2600 games using the Google reinforcement learning library Dopamine. While many RL libraries exist, this library is specifically designed with four essential features in mind:

  • Easy experimentation
  • Flexible development
  • Compact and reliable
  • Reproducible

_We believe these principles makes __Dopamine _one of the best RL learning environment available today. Additionally, we even got the library to work on Windows, which we think is quite a feat!

In my view, the visualization of any trained RL agent is an absolute must in reinforcement learning! Therefore, we will (of course) include this for our own trained agent at the very end!

We will go through all the pieces of code required (which is** minimal compared to other libraries**), but you can also find all scripts needed in the following Github repo.

1. Brief Introduction to Reinforcement Learning and Deep Q-Learning

The general premise of deep reinforcement learning is to

“derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations.”

  • Mnih et al. (2015)

As stated earlier, we will implement the DQN model by Deepmind, which only uses raw pixels and game score as input. The raw pixels are processed using convolutional neural networks similar to image classification. The primary difference lies in the objective function, which for the DQN agent is called the optimal action-value function

Image for post

where_ rₜ is the maximum sum of rewards at time t discounted by γ, obtained using a behavior policy π = P(a_∣_s)_ for each observation-action pair.

There are relatively many details to Deep Q-Learning, such as Experience Replay (Lin, 1993) and an _iterative update rule. _Thus, we refer the reader to the original paper for an excellent walk-through of the mathematical details.

One key benefit of DQN compared to previous approaches at the time (2015) was the ability to outperform existing methods for Atari 2600 games using the same set of hyperparameters and only pixel values and game score as input, clearly a tremendous achievement.

2. Installation

This post does not include instructions for installing Tensorflow, but we do want to stress that you can use both the CPU and GPU versions.

Nevertheless, assuming you are using Python 3.7.x, these are the libraries you need to install (which can all be installed via pip):

tensorflow-gpu=1.15   (or tensorflow==1.15  for CPU version)

#reinforcement-learning #q-learning #games #machine-learning #deep-learning #deep learning