Beginners Guide to Deep Learning with TensorFlow

This article is a complete beginners guide to this tool. It is not meant as an in-depth introduction but instead is designed to get you up and running to develop your first deep learning model.

In my opinion, it is difficult to find deep learning tutorials that don’t include a lot of complexity and heavy math. I personally learn best by acquiring enough knowledge to build a model that works and then learning more of the complexity as I use the method to solve more complex problems.

This tutorial is designed to demonstrate basic usage of TensorFlow with a not overly simplistic data set (to demonstrate some of the complexities encountered in a real-world machine learning problem) and give enough theory to be able to build something yourself and then develop that knowledge further with more complex articles and courses.

#machine-learning #artificial-intelligence #data-science #deep learning #tensorflow

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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

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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.

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Beginners Guide to Machine Learning on GCP

Introduction to Machine Learning

• Machine Learning is a way to use some set of algorithms to derive predictive analytics from data. It is different than Business Intelligence and Data Analytics in a sense that In BI and Data analytics Businesses make decision based on historical data, but In case of Machine Learning , Businesses predict the future based on the historical data. Example, It’s a difference between what happened to the business vs what will happen to the business.Its like making BI much smarter and scalable so that it can predict future rather than just showing the state of the business.
• **ML is based on Standard algorithms which are used to create use case specific model based on the data **. For example we can build the model to predict delivery time of the food, or we can build the model to predict the Delinquency rate in Finance business , but to build these model algorithm might be similar but the training would be different.Model training requires tones of examples (data).
• Basically you train your standard algorithm with your Input data. So algorithms are always same but trained models are different based on use cases. Your trained model will be as good as your data.

ML, AI , Deep learning ? What is the difference?

ML is type of AI

AI is a discipline , Machine Learning is tool set to achieve AI. DL is type of ML when data is unstructured like image, speech , video etc.

Barrier to Entry Has Fallen

AI & ML was daunting and with high barrier to entry until cloud become more robust and natural AI platform. Entry barrier to AI & ML has fallen significantly due to

• Increasing availability in data (big data).
• Increase in sophistication in algorithm.
• And availability of hardware and software due to cloud computing.

GCP Machine Learning Spectrum

• For Data scientist and ML experts , TensorFlow on AI platform is more natural choice since they will build their own custom ML models.
• But for the users who are not experts will potentially use Cloud AutoML or Pre-trained ready to go model.
• In case of AutoML we can trained our custom model with Google taking care of much of the operational tasks.
• Pre-trained models are the one which are already trained with tones of data and ready to be used by users to predict on their test data.

Prebuilt ML Models (No ML Expertise Needed)

• As discuss earlier , GCP has lot of Prebuilt models that are ready to use to solve common ML task . Such as image classification, Sentiment analysis.
• Most of the businesses are having many unstructured data sources such as e-mail, logs, web pages, ppt, documents, chat, comments etc.( 90% or more as per various studies)
• Now to process these unstructured data in the form of text, we should use Cloud Natural Language API.
• Similarly For common ML problems in the form of speech, video, vision we should use respective Prebuilt models.

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Beginners Guide to Debugging TensorFlow Models

If you are new to working with a deep learning framework, such as TensorFlow, there are a variety of typical errors beginners face when building and training models. Here, we explore and solve some of the most common errors to help you develop a better intuition for debugging in TensorFlow.

TensorFlow is one of the most famous deep learning models, and it is easy to learn. This article will discuss the most common errors a beginner can face while learning TensorFlow, the reasons, and how to solve these errors. We will discuss the solutions and also what experts from StackOverflow say about them.

Example 1: Wrong Input Shape for CNN layer

Example 2: Negative Dimension Size

Example ## 3: Wrong Output Shape

Example ## 4: Unknown Loss function

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Emojify - Create your own emoji with Deep Learning

Deep Learning project for beginners – Taking you closer to your Data Science dream

Emojis or avatars are ways to indicate nonverbal cues. These cues have become an essential part of online chatting, product review, brand emotion, and many more. It also lead to increasing data science research dedicated to emoji-driven storytelling.

With advancements in computer vision and deep learning, it is now possible to detect human emotions from images. In this deep learning project, we will classify human facial expressions to filter and map corresponding emojis or avatars.

About the Dataset

The FER2013 dataset ( facial expression recognition) consists of 48*48 pixel grayscale face images. The images are centered and occupy an equal amount of space. This dataset consist of facial emotions of following categories:

• 0:angry
• 1:disgust
• 2:feat
• 3:happy
• 4:sad
• 5:surprise
• 6:natural

Download Dataset: Facial Expression Recognition Dataset

Download Project Code

Before proceeding ahead, please download the source code: Emoji Creator Project Source Code

Create your emoji with Deep Learning

We will build a deep learning model to classify facial expressions from the images. Then we will map the classified emotion to an emoji or an avatar.

Facial Emotion Recognition using CNN

In the below steps will build a convolution neural network architecture and train the model on FER2013 dataset for Emotion recognition from images.

Download the dataset from the above link. Extract it in the data folder with separate train and test directories.

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