This year has been a rollercoaster. As we wrap up Augmented Startups for 2022, I would like to express my gratitude to everyone that have supported us and the work that we have been doing. This year we have seen many great innovations in AI and Computer Vision, and many great project demos on YOLOv7, Nerfs, GANs, and AI Art. It has been a very hard year as well, with the destabilization of the global economy, the war in Ukraine, COVID lockdowns in China. All of these factors determine how people spend (or not spend) their money.

In early 2018, Google launched their highly anticipated IDE: Google Colaboratory. Its fairly recent launch makes it the newest major IDE on the market. It does not require any installation, and running from the google drive on a web browser, it comes pre-installed with many popular libraries such as PyTorch, Tensorflow, and Keras. Importing unique libraries is also simple, as!pip is already installed, making the complicated activation of a terminal obsolete. Google Colab is also linked to the Google Drive, meaning it is saved onto the cloud.

Google Colaboratory, often referred to as colab, is a product created by Google to allow anyone to create and run python code in the browser. It has many standard machine and data science libraries built-in including pandas and scikit-learn. You can also install practically any other python library for use in each notebook. To access colab you need to sign up for a Google account and this then gives you free access to the notebook environment and computing resources that include GPU's. Let's walk through a quick demo.

Design, Develop and Train the model In this course, we provide the step-by-step approach for building a Linear Regression model using TensorFlow with Python. In this course, we provide the step-by-step approach for building a Linear Regression model using TensorFlow with Python. In the beginning, we give a high-level introduction to Artificial Intelligence and Machine Learning. We develop the entire system in Google Colaboratory using TensorFlow. So, we have a lecture each on Introduction to Google Colaboratory and Introduction to TensorFlow.

In this hands-on project, we will train a Bidirectional Neural Network and LSTM based deep learning model to detect fake news from a given news corpus. This project could be practically used by any media company to automatically predict whether the circulating news is fake or not. The process could be done automatically without having humans manually review thousands of news related articles. This project is for anyone with foundation in programming and machine learning who wants to develop Data science and Machine learning projects but having limited resources on their computer and limited time. You will learn how to use the Google Colaboratory via your web browser to develop a Fake and Real News Detection Data Science Project.

Implementing a deep learning attention based classification model proposed in the paper "Learn To Pay Attention" published in ICLR 2018 conference. The basic idea behind attention models is to focus on that parts of a problem which are important. Such a model was introduced in 2014 and was mainly focused on solving NLP problem but eventually was found to be useful in the field of computer vision. Jetley et.al in the paper "Learn To Pay Attention" used attention based mechanism to solve simple image classification problem. The most important concept discused in this paper would be'attention maps' which is a scalar matrix that represents activations of different locations of an image with respect to a target. With the help of attention maps the CNNs will eventually learn which part of an image is important for a particaular task.

For this week's ML practitioner's series, Analytics India Magazine got in touch with Hiroki Yamamoto (tereka), a Kaggle Master. Hiroki is currently working as a data scientist and is ranked in the top 100 of the world's largest platforms for data science competitions– Kaggle. In this interview, Hiroki shares his experience of competing on Kaggle and how it has helped in growing as a data scientist. Hiroki: I got a master's degree in information technology back in 2015. During my graduation, I have worked on image processing research using deep learning -- for example, autoencoders.

"We want to use AI to augment the abilities of people, to enable us to accomplish more and to allow us to spend more time on our creative endeavors." Calling Google just a search giant would be an understatement with how quickly it grew from a mere search engine to a driving force behind innovations in several key IT sectors. Over the past couple of years, Google has planted its roots into almost everything digital, be it consumer electronics such as smartphones, tablets, laptops, its underlying software such as Android and Chrome OS or the smart software backed by Google's AI. Google has been actively innovating in the smart software industry. Backed by its expertise in search and analytical data acquired over the years have helped Google create various tools like TensorFlow, ML Kit, Cloud AI, and many more for enthusiasts and beginners alike who are trying to understand the capabilities of AI.

Google Colaboratory is a free online cloud-based Jupyter notebook environment that allows us to train our machine learning and deep learning models on CPUs, GPUs, and TPUs. Here's what I truly love about Colab. It does not matter which computer you have, what it's configuration is, and how ancient it might be. You can still use Google Colab! All you need is a Google account and a web browser.

With the growing value of big data and machine learning, Data Science attracted interest from professionals of various areas of expertise. You are one of these professionals, and then you studied linear algebra, calculus, probabilities, machine learning, and now you want to put this knowledge in practice. All you want to do is to load some small data, perform some exploration, create some visualization, and train a simple model. Then you go to the Internet searching for the right tool to start your brand new data science project, and you find a lot of options. You install new software, libraries, and spend some time reading tutorials. But you still can't decide which tool to use.