Understanding Computer Vision with Deep Learning – Free Webinar

CloudxLab conducted a successful webinar on “Introduction to Machine Learning” on the 15th of October, 2019.  It was a 2-hour session in which the instructor explained the concepts based on Understanding Computer Vision with Deep Learning.

More than 250 learners around the globe attended the webinar. The participants were from countries namely; United States, Canada, Australia, Indonesia, India, Thailand, Philippines, Malaysia, Macao, Japan, Hong Kong, Singapore, United Kingdom, Saudi Arabia, Nepal, & New Zealand.

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Fashion-MNIST using Deep Learning with TensorFlow Keras

A few months back, I had presented results of my experiments with Fashion-MNIST using Machine Learning algorithms which you can find in the below mentioned blog:

https://cloudxlab.com/blog/fashion-mnist-using-machine-learning/

In the current article, I am presenting the results of my experiments with Fashion-MNIST using Deep Learning (Convolutional Neural Network – CNN) which I have implemented using  TensorFlow Keras APIs (version 2.1.6-tf).

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Things to Consider While Managing Machine Learning Projects

Generally, Machine Learning (or Deep Learning) projects are quite unique and also different from traditional web application projects due to the inherent complexity involved with them.

The goal of this article is, not to go through full project management life cycle, but to discuss a few complexities and finer points which may impact different project management phases and aspects of a Machine Learning(or Deep Learning) project, and, which should be taken care of, to avoid any surprises later.

Below is a quick ready reckoner for the topics that we will be discussing in this article.

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Conference on Computer Vision at Google Asia, Singapore

The deep learning algorithms and frameworks have changed the approach to computer vision entirely. With the recent development in computer vision with Convolutional Neural Networks such as Yolo, a new era has begun. It would open doors to new industries as well as personal applications.

After the successful bootcamps held at IIT Bombay, NUS Singapore, RV College of Engineering, etc, CloudxLab in collaboration with IoTSG and Google Asia conducted a successful conference on Understanding Computer Vision with AI using Tensorflow on May 11, 2019, at Google Asia, Singapore office.

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Creating AI Based Cameraman

Whenever we have our live talks of CloudxLab, in presentations or in a conference, we want to live stream and record it. The main challenge that occurs is the presenter gets out of focus as the presenter moves. And for us, hiring a cameraman for three hours of a session is not a viable option. So, we thought of creating an AI-based pan and tilt platform which will keep the camera focussed on speaker.

So, Here are the step-by-step instructions to create such a camera along with the code needed.

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Regression with Neural Networks using TensorFlow Keras API

As part of this blog post, I am going to walk you through how an Artificial Neural Network figures out a complex relationship in data by itself without much of our hand-holding. You should modify the data generation function and observe if it is able to predict the result correctly. I am going to use the Keras API of TensorFlow. Keras API makes it really easy to create Deep Learning models.

Machine learning is about computer figuring out relationships in data by itself as opposed to programmers figuring out and writing code/rules. Machine learning generally is categorized into two types: Supervised and Unsupervised. In supervised, we have the supervision available. And supervised learning is further classified into Regression and Classification. In classification, we have training data with features and labels and the machine should learn from this training data on how to label a record. In regression, the computer/machine should be able to predict a value – mostly numeric. An example of Regression is predicting the salary of a person based on various attributes: age, years of experience, the domain of expertise, gender.

The notebook having all the code is available here on GitHub as part of cloudxlab repository at the location deep_learning/tensorflow_keras_regression.ipynb . I am going to walk you through the code from this notebook here.

Generate Data: Here we are going to generate some data using our own function. This function is a non-linear function and a usual line fitting may not work for such a function

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Deploying Machine Learning model in production

In this article, I am going to explain steps to deploy a trained and tested Machine Learning model in production environment.

Though, this article talks about Machine Learning model, the same steps apply to Deep Learning model too.

Below is a typical setup for deployment of a Machine Learning model, details of which we will be discussing in this article.

Process to build and deploy a REST service (for ML model) in production
Process to build and deploy a REST service (for ML model) in production

The complete code for creating a REST service for your Machine Learning model can be found at the below link:

https://github.com/cloudxlab/ml/tree/master/projects/deploy_mnist

Let us say, you have trained, fine-tuned and tested Machine Learning(ML) model – sgd_clf, which was trained and tested using SGD Classifier on MNIST dataset.  And now you want to deploy it in production, so that consumers of this model could use it. What are different options you have to deploy your ML model in production?

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One-on-one discussion on Gradient Descent

Usually, the learners from our classes schedule 1-on-1 discussions with the mentors to clarify their doubts. So, thought of sharing the video of one of these 1-on-1 discussions that one of our CloudxLab learner – Leo – had with Sandeep last week.

Below are the questions from the same discussion.

You can go through the detailed discussion which happened around these questions, in the attached video below.

One-on-one discussion with Sandeep on Gradient Descent
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How To Optimise A Neural Network?

When we are solving an industry problem involving neural networks, very often we end up with bad performance. Here are some suggestions on what should be done in order to improve the performance.

Is your model underfitting or overfitting?

You must break down the input data set into two parts – training and test. The general practice is to have 80% for training and 20% for testing.

You should train your neural network with the training set and test with the testing set. This sounds like common sense but we often skip it.

Compare the performance (MSE in case of regression and accuracy/f1/recall/precision in case of classification) of your model with the training set and with the test set.

If it is performing badly for both test and training it is underfitting and if it is performing great for the training set but not test set, it is overfitting.

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