Angela  Dickens

Angela Dickens

1597251300

Deep Learning Image Classification with Fastai

TL;DR

If you are beginning to feel burnt out on learning a subject it is often beneficial to take a step out of the weeds. I like to build something fun and easy to regain positive momentum on my learning journey. This is one project that helped get my creative juices flowing again. Load my notebook from my GitHub repository into Google Colab and upload the Kaggle data set to learn how to build an image classifier using the fastai software! Don’t forget to set the hardware accelerator to GPU!

P.S. Directly upload notebook to Colab by going to File →Upload Notebook → GitHub Tab→BSamaha/Chest-Xray-FastAI

Image for post

Image for post

How to easily upload a GitHub Notebook to Google Colab


Intro

The Dunning-Kruger Effect

You have set a goal such as wanting to be a data scientist or data engineer. Your passion and energy are through the roof as you rifle through all the material on the subject you can find. Perhaps you even started a coding boot camp or a Coursera class to be your guide on your path to data mastery. Your brain becomes saturated with dopamine as you quickly rack up quick wins. First, it was basic python programming. Then you made your first linear regression with sci-kit learn — machine learning isn’t so bad after all! You quickly absorbed all information thrown at you. Nothing can stop you.

I’ve been there, and I still go there. I am human after all. I’m referring to the peak of “Mt. Stupid” in The Dunning-Kruger Effect. Linear regression was easy with a perfectly curated data set, but that’s not reality. Finally, after about a month or two of flying through the material with ease, you hit a wall named statistics. You grind through it and barely make it out — except you feel like you haven’t retained anything. Do people remember all this distribution stuff? Spoiler Alert: yes, and you will be questioned about them. As you pursue on you realize your python skills are nowhere near where they should be. You decide to look at a couple of job postings and then it hits you. You don’t have an advanced graduate degree in computer science, and you don’t know how to pronounce some requirements of the job much less know what they are — Hadoop? Welcome to the Valley of Despair. You want to quit and move on with your life.

#deep-learning #machine-learning #deep learning

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Buddha Community

Deep Learning Image Classification with Fastai
Marget D

Marget D

1618317562

Top Deep Learning Development Services | Hire Deep Learning Developer

View more: https://www.inexture.com/services/deep-learning-development/

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

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

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

Mikel  Okuneva

Mikel Okuneva

1603735200

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

Agnes  Sauer

Agnes Sauer

1596328500

All about images -Types of Images:

Everything we see around us is nothing but an Image. we capture them using our mobile camera. In Signal Processing terms, Image is a signal which conveys some information. First I will tell you about what is a signal? how many types are they? Later part of this blog I will tell you about the images.

We are saying that image is signal. Signals are carry some information. It may be useful information or random noise. In Mathematics, Signal is function which depends on independent variables. The variables which are responsible for the altering the signal are called independent Variables. we have multidimensional signals. Here you will know about only three types of signals which are mainly used in edge cutting techniques such as Image processing, Computer Vision, Machine Learning, Deep Learning.

  • 1D signal: Signals which has only one independent variable. Audio signals are the perfect example. It depends on the time. For instance, if you change time of an audio clip, you will listen sound at that particular time.
  • 2D signal: Signals which depends on two independent variables. Image is an 2D signal as its information is only depends on its length and width.
  • 3D signals : Signals which depends on three independent variables. Videos are the best examples for this. It is just motion of images with respect to time. Here image’s length and width are two independent variables and time is the third one.

Types of Images:

  • Analog Images: These are natural images. The images which we see with our eye all are Analog image such as all physical objects. It has continuous values. Its amplitude is infinite.
  • **Digital images: **By quantizing the analog images we can produce the digital images. But now-a-days, mostly all cameras produce digital images only. In digital Images, All values are discrete. Each location will have finite amplitude. Mostly we are using digital images for processing.

Image for post

Image for post

Every digital image will have group of pixels. Its coordinate system is starts from top coroner

Digital images contains stack of small rectangles. Each rectangle we call as Pixel. Pixel is the smallest unit in the image.Each Pixel will have particular value that is intensity. this intensity value is produced by the combination of colors. We have millions of colors. But our eye is perceive only three colors and their combinations. Those color we call primary colors i.e., Red, Green and Blue.

Image for post

Image for post

Why only those three colors ???

Do not think much. the reason is as our human eye has only three color receptors. Different combinations in the stimulation of the receptors enable the human eye to distinguish nearly 350000 colors

Lets move to our image topic:

As of now, we knew that image intensity values is combination of Red, Green and Blue. Each pixel in color image will have these three color channels. Generally, we represent each color value in 8 bits i.e., one byte.

Now, you can say how many bits will require at each pixel. We have 3 colors at each pixel and each color value will be stored in 8 bits. Then each pixel will have 24 bits. This 24 bit color image will display 2**24 different colors.

Now you have a question. how much memory does it require to store RGB image of shape 256*256 ???I think so explanation is not required, if you want to clear explanation please comment below.

#machine-learning #computer-vision #image-processing #deep-learning #image #deep learning

Angela  Dickens

Angela Dickens

1597251300

Deep Learning Image Classification with Fastai

TL;DR

If you are beginning to feel burnt out on learning a subject it is often beneficial to take a step out of the weeds. I like to build something fun and easy to regain positive momentum on my learning journey. This is one project that helped get my creative juices flowing again. Load my notebook from my GitHub repository into Google Colab and upload the Kaggle data set to learn how to build an image classifier using the fastai software! Don’t forget to set the hardware accelerator to GPU!

P.S. Directly upload notebook to Colab by going to File →Upload Notebook → GitHub Tab→BSamaha/Chest-Xray-FastAI

Image for post

Image for post

How to easily upload a GitHub Notebook to Google Colab


Intro

The Dunning-Kruger Effect

You have set a goal such as wanting to be a data scientist or data engineer. Your passion and energy are through the roof as you rifle through all the material on the subject you can find. Perhaps you even started a coding boot camp or a Coursera class to be your guide on your path to data mastery. Your brain becomes saturated with dopamine as you quickly rack up quick wins. First, it was basic python programming. Then you made your first linear regression with sci-kit learn — machine learning isn’t so bad after all! You quickly absorbed all information thrown at you. Nothing can stop you.

I’ve been there, and I still go there. I am human after all. I’m referring to the peak of “Mt. Stupid” in The Dunning-Kruger Effect. Linear regression was easy with a perfectly curated data set, but that’s not reality. Finally, after about a month or two of flying through the material with ease, you hit a wall named statistics. You grind through it and barely make it out — except you feel like you haven’t retained anything. Do people remember all this distribution stuff? Spoiler Alert: yes, and you will be questioned about them. As you pursue on you realize your python skills are nowhere near where they should be. You decide to look at a couple of job postings and then it hits you. You don’t have an advanced graduate degree in computer science, and you don’t know how to pronounce some requirements of the job much less know what they are — Hadoop? Welcome to the Valley of Despair. You want to quit and move on with your life.

#deep-learning #machine-learning #deep learning