Maximize Productivity and Efficiency with the Help of ChatGPT, Google Keep and Google Calender
A computer or smartphone with internet connection.
In this course, we will dive into the world of time management and productivity using the powerful combination of ChatGPT, Google Keep and Google Calendar. You will learn how to effectively utilize these tools to identify important tasks, manage your time, and increase productivity. The course is designed to give you a step by step approach to mastering time management.
First, we will start by teaching you how to list and prioritize tasks. This is the foundation of time management and will ensure that you are focusing on the most important tasks first. You will learn how to use ChatGPT and other online tools to automate tasks, streamline your workflow and increase your productivity.
Next, we will teach you how to create a schedule that is tailored to your needs. This will help you stay organized and on track throughout the day, week, or month. We will also teach you how to eliminate distractions and focus on getting the work done.
In addition, you will learn how to use the powerful capabilities of ChatGPT, Google Keep and Google Calendar to increase your productivity and work output. We will teach you how to use these tools to automate tasks, set reminders, and stay organized.
By the end of this course, you will have the skills and knowledge to use ChatGPT, Google Keep, and Google Calendar for everyday time management and achieve greater efficiency and productivity in your personal and professional life. With this course, you will be able to manage your time more effectively, increase productivity, and achieve your goals.
Who this course is for:
This course is for anyone looking to improve their time management and productivity using ChatGPT, Google Keep, and Google Calendar or any other apps.
When you want to learn a new technology for professional use, there are two mutually exclusive options, either you learn it yourself or you go for instructor based training.
Self learning is least expensive but lot of time results in wasting time in finding right contents, setting up the environment , troubleshooting issues and may make you give up in the middle.
Instructor based training can be expensive at times and need your time commitment.
This course combines the best of both these options. The course is based on one of the most famous books in the field “Python Machine Learning (2nd Ed.)” by Sebastian Raschka and Vahid Mirjalili and provides you video tutorials on how to understand the AI/ML concepts from the books by providing out of box virtual machine with demo examples for each chapter in the book and complete preinstalled setup to execute the code.
You learn the concepts by self learning and get hands on executing the sample code in the virtual machine.
The demo covers following concepts:
Machine Learning – Giving Computers the Ability to Learn from Data
Training Machine Learning Algorithms for Classification
A Tour of Machine Learning Classifiers Using Scikit-Learn
Building Good Training Sets – Data Pre-Processing
Compressing Data via Dimensionality Reduction
Learning Best Practices for Model Evaluation & Hyperparameter Optimization
Combining Different Models for Ensemble Learning
Applying Machine Learning to Sentiment Analysis
Embedding a Machine Learning Model into a Web Application
Predicting Continuous Target Variables with Regression Analysis
Working with Unlabeled Data – Clustering Analysis
Implementing a Multi-layer Artificial Neural Network from Scratch
Parallelizing Neural Network Training with TensorFlow
Going Deeper: The Mechanics of TensorFlow
Classifying Images with Deep Convolutional Neural Networks
Modeling Sequential Data Using Recurrent Neural Networks
In addition to the preinstalled setup and demos, the VM also comes with:
Jupyter notebook for web based interactive development
JupyterHub for multiuser notebook environment to allow multiple users to simultaneously do development
Visual studio code IDE
The VM is available on :
Google Cloud Platform
Who this course is for:
Python developers who are intrested in learning Artificial Intelligence and Machine Learning
Apply your Deep Learning skills and create your own end-to-end Image Search engine!
Basic conceptual understanding of Convolutional Neural Networks (CNN)
Basic knowledge of Deep Learning ins mandatory
(optional) Previous coding experience with TensorFlow
Artificial intelligence is one of the fastest growing fields of computer science today and the demand for excellent AI Engineers is increasing day in and day out. This course will help you stay competitive in the AI job market by teaching you how to create a Deep Learning End-to-End product on your own.
Most courses focus on the basics of Deep Learning and teach you about the very basics of different models. In this course, however, you will learn how to write a whole End-to-End pipeline, from data preprocessing across choosing the right hyper-parameters, to showing your users results in a browser.
The case that we will tackle in this course is an engine for Image to Image Search.
Why should you take this course?
This course is not focused on teaching you Neural Networks (ANNs, CNNs, RNNs…), but teaching you how to apply them in real world cases.
If you haven’t worked on a product that uses Deep Learning before, this is the perfect course for you! Throughout the course we will work together on the Image to Image Search engine, starting from ground zero – image preprocessing, creating a model, training it, then testing. After that we will create a simple web application and use it to serve our model in production.
Another cool thing about this course is that we will use multiple programming languages to create the whole application around the model itself. This will make you not only a better AI Engineer but also get you on the path towards becoming a Full stack AI Engineer.
After taking this course you will guarantee yourself to be one step closer to landing your dream job as an AI/ML Engineer by having your own AI product/project in your portfolio.
Libraries/Tools used in the course:
The whole Deep learning back-end of our pipeline will be built using Tensorflow 1.10.0. For some image preprocessing task we will use some basic functionality from OpenCV, the most important Python library for image processing tasks!
For the app’s back-end (model handling, image uploading, page navigation, etc.) we will use the Flask python framework.
Who is this course for?
As you can see the course is meant to teach you how to create your own Deep Learning product from scratch.
If you are just starting out with Deep Learning, this course might be too hard for you. But if you like challenges, I do recommend following it. Although I will not be explaining the meat of Neural Networks (ANNs, CNNs), I will explain most concepts in great detail, so even if you are a total beginner you should be able to follow with the help of your peers or my help through the comments section.
If you have Deep Learning experience and want to move it to the next level you will find this course very useful! You can consider it as a level up for your skills by putting your already great skills to new use. At the end of the course you will not only have learned how to create a working End-to-End pipeline, but also hold proof of your skills for potential employers!
The conclusion is this – this is very rare opportunity, not only to learn Deep Learning concepts, but also how to apply that knowledge and create your own web application (as a complete product) from scratch.
I hope to see you in class!
Who this course is for:
Everyone interested in Deep Learning
Students who already have basic/intermediate understanding of the backpropagation algorithm
Anyone interested in raising their Deep Learning knowledge to the next level
Any intermediate level students who have a basic understanding of Neural Networks (Artificial Neural Networks, Convolutional Neural Networks)
Anyone who likes coding and wants to create their own product from scratch
Any students who are interested in learning how to move Deep Learning models from testing to production environments
Any entrepreneur who wants to create their own Deep Learning based product
Wildlife experts are surprised to see animal populations recovering across eastern Australia following the devastating Black Summer bushfires.
WWF is tracking the recovery of animal species after the Black Summer bushfires in 2019-20
Artificial intelligence and machine learning are being used to identify animal species after they are photographed
Researchers say animals are recovering “surprisingly” well after the fires
The Eyes on Recovery project conducted by the World Wildlife Fund (WWF), Conservation International, and local land managers and research organisations is using artificial intelligence (AI) to monitor the recovery of animal populations since the 2019–20 bushfires.
Across eight fire-affected regions in South Australia, Victoria, New South Wales and Victoria 1,100 sensor cameras were installed.
Bushfire recovery research regions:
Kangaroo Island, SA
East Gippsland, Vic
South Coast, NSW
Southern Ranges, NSW
Blue Mountains, NSW
Central Coast, NSW
North Coast, NSW
South East Queensland
Researchers collected more than 7 million photos that were analysed using AI technology to identify more than 150 native animals.
WWF program coordinator Emma Spencer said the results have generally been positive.
“Even deep within those heavily fire-impacted areas there are signs of recovery, from threatened species like koalas,” she said.
“In the East Gippsland area, in particular, we’ve been getting a lot of images of our beautiful long-nosed potoroos, which are endangered in the region.”
Billions of animals lost
Nearly 3 billion koalas, kangaroos and other animals were predicted to have been killed or displaced as a result of the Black Summer bushfires.
A WWF Australia study labelled it the worst single event for wildlife in Australia, and among the worst in the world, and that it likely pushed some species into extinction.
Dr Spencer has been amazed to see that animal populations are beginning to recover even in decimated habitats that were at the centre of bushfires’ destruction.
“I walked out in a lot of these areas, right after the fires,” she said.
“It’s very hard to imagine sometimes when you’re walking through that blackened landscape that anything could have survived and moved back in following the fire.
“There was a deal of surprise when, especially in those sites that were … so deep in the fire-impacted country, that we were getting not just one, but a few of those threatened species returning.”
The photos are analysed by a program that uses AI and machine learning (ML) to identify species.
It is the first time the platform has been tested on Australian wildlife and, after training, it can recognise species with about 90 per cent accuracy.
Using the technology means reviewing the images and supporting the wildlife is a more “efficient” process, Dr Spencer says.
“That means we know where the potoroos are straight away, we know where the brush-tailed rock wallabies are straight away,” she said.
“And that means we can get out and actually enact management activities, go out there and conserve those species sooner rather than later.”
Interventions to help wildlife
The data helps to inform a range of management and conservation interventions, including controlling invasive species, providing temporary artificial refuges for animals that have lost their homes and rewilding areas.
Dr Spencer says species are likely recovering well in East Gippsland due to the region’s small population of feral animals compared to other parts of eastern Australia.
“So that’s perhaps why we’re seeing such strong recovery of species like the long-footed potoroo and the long-nosed potoroo for instance,” she said.
Terrestrial ecologist at the University of Sydney Christopher Dickman said the effect of the bushfires on feral animals was largely unclear due to a lack of data except for Kangaroo Island.
“They did appear to have a substantial decline in cat activity in the wake of the fires,” he said.
“So it seems the fires had a negative impact on feral cats.”
Professor Dickman says feral animals are a larger threat post-bushfires as they can disrupt the recovery of native fauna that were already vulnerable.
“Foxes and cats are very mobile, whereas the prey species they tend to select are much smaller,” he said.
“After a fire, the fire reduces populations by a certain amount across the board.
“Foxes and cats are much better able to respond by moving in from unburnt areas into the burnt areas than the prey species simply because of their mobility.”
Monitoring to continue
The research program ends in July but many of the cameras will remain, with WWF’s ground partners continuing to monitor the wildlife recovery.
Dr Spencer says it’s important to keep monitoring the habitats.
Efforts to save greater glider after Black Summer
A new study has offered a glimpse of hope for researchers battling to conserve the endangered southern greater glider.
“There wasn’t a lot of monitoring going on before the fires,” she said.
“We actually weren’t able to really get a good idea on what we’d lost post-fires.
“So if we have another fire event, which we eventually will, we’ll know exactly how much we’ve lost and how to recover it.”
Dr Spencer said Black Summer was not an “isolated event” and that AI technology could be used to assess and monitor wildlife after future natural disasters.
“We’re expecting to get more frequent severe fires like this, but we’re also seeing lots of floods and then there’s all the droughts as well,” she said.
“This technology can be applied to help us more rapidly monitor wildlife following all those peaks of disaster events.”
But Professor Dickman says the program does have some limitations such as its inability to capture all animals.
“Some of the small reptiles, some of the frogs, greater gliders, yellowbelly gliders — they tend to spend more time in the treetops, and we’re less likely to see them on camera,” he said.
As the world progresses towards digitalization, more people are adopting Artificial Intelligence (AI). The pandemic has accelerated this adoption. There are predictions that computers and robots will become more capable of comprehending multiple languages and knowledge.
Machine learning is an association of artificial intelligence (AI) and computer science that operates data and algorithms to emulate how humans learn, gradually enhancing its accuracy, defined mainly as a machine’s capability to mimic intelligent human demeanour.
Machine Learning involves machines learning on their own without explicit programming. These systems use quality data to build various machine-learning models with the help of algorithms. The selection of algorithms is determined by the nature of the data and the specific task that needs to be accomplished.
Dark AI Scenarios and Malevolent AI: It’s important to remember that everything has two sides much like a coin for everything good , will have something bad associated with it!, including machine learning.
While it has become a popular solution for many applications, hackers and crackers are finding ways to exploit these approaches. Although machine learning can bring innovation and adaptation to various sectors, it raises concerns and potential issues.
With powerful AI applications, personal secrets have the potential to be unravelled at the behest of Artificial intelligence much against our consent. Protecting personal information is crucial, and it’s important to remain vigilant.
While certain technologies were designed with good intentions, they can be misused if they end up in the wrong hands. As we explore the neverending possibilities of this innovative technology,
it’s important to remain mindful of its ramifications and negative impacts. While using Artificial Intelligence can be highly beneficial, it can pose a significant security and privacy risk.
Label flipping involves swapping the expected outcomes. A poisoning attack occurs when the attacker adds inadequate data to your model’s training dataset, leading it to learn inappropriate information. The most anticipated result of a poisoning attack is that the model’s boundary limits shift somehow.
threats with the Machine learning model: Adversarial Examples/Evasion Attack: One crucial security threat to machine learning systems is Adversarial Examples or Evasion Attacks, which are extensively studied. This attack involves manipulating the input or testing data to make the machine learning system predict incorrect information.
This compromises the system’s integrity, and the confidence in the system is affected. It has been noted that a system that overfits data is vulnerable to evasion attacks.
If a hacker intercepts the interaction between the model and the interface responsible for showing results, they can display manipulated information. This type of attack is named the output integrity attack. Due to our absence of understanding of the actual inner working of a machine learning system theoretically, it becomes difficult to predict the natural result. Hence, when the system has shown the output, it is taken at face value. The attacker can control this naivety by compromising the integrity of the production.
Although machine learning algorithms have existed for decades, their popularity has increased with the growth of artificial intelligence, particularly in deep learning models that power today’s most advanced AI applications. Many major vendors, including Amazon, Google, Microsoft, IBM, and others, compete to sign up customers for their machine learning platforms.
These platforms cover machine-learning activities, including data collection, preparation, classification, model building, training, and application development. There is a growing trend towards utilizing a critical technology that many businesses across various industries are steadily adopting at a rapid pace.