Trending November 2023 # Can’T Sign In To Facetime On Mac? Check These Solutions Out • Mactips # Suggested December 2023 # Top 17 Popular

You are reading the article Can’T Sign In To Facetime On Mac? Check These Solutions Out • Mactips updated in November 2023 on the website Hatcungthantuong.com. We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested December 2023 Can’T Sign In To Facetime On Mac? Check These Solutions Out • Mactips

Can’t sign in to FaceTime on Mac? Check these solutions out

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FaceTime is a great application for video calls, but some users reported that they couldn’t sign in to FaceTime on MacBook. This can be a problem, but there’s a way to fix this issue, and today we’re going to show you how to do it.

What to do if you can’t sign in to FaceTime on Mac? 1. Check your Internet connection and status of Apple’s services

The first thing that you need to check is your Internet connection. If your Internet connection is working properly, you need to check the Apple System Status.

Sometimes certain services can experience issues, and if one of these required services is having problems, you’ll have to wait for Apple to address the server-side issue.

2. Create an app-specific password for FaceTime

Sometimes two-step verification can cause problems with FaceTime Time and prevent you from signing in. To fix that, it’s required that you create a specific password for FaceTime. This is rather simple and you can do it by following these steps:

Now select Generate an

App-Specific

Password.

After generating the password, try

signing in

FaceTime

using your Apple ID and the newly generated password.

If you can’t sign in to FaceTime on your MacBook, it’s a good idea to check your date and time. Sometimes if the date or time isn’t correct on your device, you might encounter all sorts of issues. If date or time isn’t correct, update it by doing the following:

Open the Apple menu and navigate to System Preferences.

Now select Date & Time.

After adjusting the date and time, check if the problem is still there.

FAQ: Learn more about using FaceTime on MacBook

Why isn’t my camera working on my Macbook?

The camera might not be working on your MacBook because of an app that might be using it already, or antivirus software that is blocking access to the camera.

Why is my FaceTime not working on Mac?

The most common reason for this issue appearing on Mac is a problematic internet network connection.

How do I set up FaceTime on my laptop?

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Can Chatgpt Do My Homework?

Last Updated on April 21, 2023

Whether you are a student or just a bit curious, you may be wondering, can ChatGPT do my homework? To be honest, there is no reason why ChatGPT can’t do your homework. But, whether it can do it well is another story.

ChatGPT is a very powerful and versatile online AI chatbot. You can quite literally ask the model anything and it will generate a text response to answer your request.

There are headlines about the model’s ability to write degree-level essays, pass university exams and succeed in Google’s coding interview. It is pretty clear that this AI model is very capable but should you use it for your homework?

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Can An Antivirus Stop Hacking?

Also Read: Can An Antivirus Slow Down Games On Your PC?

How Does An Antivirus Protect You Against Hackers?

In simple terms, hackers actualize their nefarious intentions by using malicious lines of codes that they pack inside a file. Then through a variety of online and offline mediums, they drive users into downloading their malicious files. An Antivirus perceives this, scans the file beforehand much before it can cause any further damage to your computer.

– Database Regarding Viruses Is Regularly Updated

Let’s put it this way, at this very moment, some very shrewd hacker might be developing a malicious threat that he or she thinks that no expert can catch. What such hackers don’t know is that they are putting themselves up against an updated database that already contains descriptions of the threats that such miscreants create and even the method that can combat such threats. Though, there had been instances in the past when hackers did dodge even the most sophisticated Antivirus solutions. However, if you have a good Antivirus solution, the chances of this are next to none.

– By Offering Real-Time Protection Against Malicious Threats

– By Offering Security Against Zero-Day Threats

When a hacker can sniff the vulnerability in software much before the software’s developers, he or she will most likely exploit it, this becomes a zero-day threat or a zero-day attack. An Antivirus is well aware of these attacks and doesn’t hold back. Thanks to the frequently updated database and an equally efficient engine, the Antivirus nips the threat in the bud.

– Web And Firewall Protection

So, this is a very interesting and powerful module that a good quality Antivirus tool packs inside. Hackers often don’t spare unsecured or poorly developed websites. Your mere visiting the website can make you a hacker’s target. From gaining access to your IP to stealing your credentials such as credit/ debit card and other details, there’s a lot that can happen. Most Antivirus utilities come with web protection capability. The feature prompts you not to visit a malicious website.

Let’s move further and talk about Firewall protection. There have been instances of SQL injection attacks and some others where the attacker was successfully able to bypass the web application firewall (WAF). That’s not the only case where a firewall may fail in protecting you from a hacker. Let’s assume that due to some reason your firewall isn’t configured correctly or the policies in it are too permissive. In such a case, a data breach is likely evident. A modern-day Antivirus is well aware of this and keeps even your firewall intact and something that no hacker could invade.

Example of How An Antivirus Application Renders Strong Protection Against Hackers

Now that we know how an Antivirus protects you against hackers, let’s have a look at an example to testify the efficacy of an Antivirus. If we consider the Windows operating system, Systweak Antivirus is one of the most renowned and trusted antivirus solutions. It is capable of delivering protection against a variety of malicious threats such as Trojan, ransomware, adware, spyware, and a variety of other malicious threats. You can check out the full review of Systweak Antivirus here to get a better look at its features, how it works, its pros and cons (if any). But to exemplify the above points, let’s just have a glimpse of the tool –

What Features Does Systweak Antivirus Offers

Powerful scanning engine

Real-time protection against malicious threats

A frequently updated database

Multiple scanning modes – Quick, Deep, and Custom

Website and firewall protection

A lightweight piece of software

Even helps keep track of unwanted or unsafe startup items

How To Use Systweak Antivirus?

Download, install and run Systweak Antivirus

If Not An Antivirus, Then What Else Can Stop Hacking?

It doesn’t matter what device or operating system you have. In today’s digital day and age, an Antivirus should be an integral part of your computer and the very concept of not having one should remain in your theories because the moment your smartphone or computer is bereft of real-time protection, a hacker will possibly catch you off-guard and inject the virus.

But, here are certain points that as a prudent use of your device, you should exercise –

Never visit an unsafe website, leave aside downloading files from there

Never miss an update either from your operating system or the software on your device

When on a public – Wi-Fi and even otherwise, use a VPN where you feel a hacker might exploit your IP address

Store your passwords and other crucial credentials behind a vault in a password manager

Make full use of two-factor authentication to log in on multiple devices. And, if you can use biometrics like facial recognition or fingerprint scanning, go for it.

Wrapping Up:

Quick Reaction:

About the author

Sarang Bhargava

Can You Become A Self

Don’t worry about getting a degree in Python, you can become a self-taught Python developer

Most of the job descriptions would carry a requirement for a certificate in Python programming, or a degree in computer programming, computer science, or a related field, right? The flexibility they include in the description is meant for people with a similar background but having enough knowledge in the skills mentioned therein. Yes, they are an ever-growing breed of self-taught software developers. Python is one of the most in-demand programming languages, and given the lack of qualified faculty, aspirants are teaching themselves the language to make a killing out of the abundance of job opportunities in the market for python developers. Don’t worry about getting a degree in Python, you can become a self-taught Python developer as well.

Learn a Programming Language

From Javascript to Python, there are several programming languages to choose from when you’re learning computer science. Since you want to become a Python developer you will choose Python.

Learn Data Structures and Algorithms

Once you’ve mastered the Python programming language, the next step is to familiarize yourself with data structures and algorithms. Data structures improve the efficiency of storing, fetching, and organizing data. Algorithms, which you can think of as assets of operations and instructions, are applied to data structures to get the desired output. Several top tech companies such as Google and Microsoft often have data structure and algorithm questions in their coding interviews.

Git and GitHub for Storage and Sharing

Building on the popularity of open-source tools and programming amongst developers, Git and GitHub are two collaborative tools that are useful for Python developers. Many developers use Github to store their code and share it with a larger community of users.

SQL and Database Management

In addition to learning the Python programming language, Python developers should also learn SQL and database management. SQL is a programming language used to develop a database and communicate with the information and data stored inside. Python developers benefit from learning SQL and database management to create the data architecture of a website or a platform.

Finally, aspiring Python developers should also learn developer tools and frameworks to ease the process of developing websites, software, or mobile applications. Within the Python programming language, there are numerous frameworks for web development that are important to learn. Like libraries, frameworks ease developing a product by offering built-in tools and specialized functions to create a particular product or service.

Now, you might think about where to learn all these? There’s a way there too. There are so many online programs coding boot camps, youtube lectures, etc which will help you build your knowledge.

Learn Online

Traditional education is slowly dying. Not everyone has time or money to spend on a four-year college degree for the knowledge that can be readily accessed online. Currently, the perfect resources to learn something are not books but online courses. (Books can be good add-on supplements)

Coding boot camps are a surefire way to build one’s skills and capabilities in any career. For students and professionals with an investment in programming, coding boot camps are also a way to build a career without a degree. One way to become a Python developer without a degree is by learning the essential skills of python development through coding boot camps and certificate programs.

But you need to ensure that only learning will not help you. You have to build something practical that you can present proudly.

Build something meaningful with your expertise. Once you become comfortable enough with technology, try to come up with a project idea where you get to implement whatever you are learning. For example, you can build a blog website for a client, you can create a photo slider, a to-do app or a fun game, or some fancy web features like a hover effect or unlimited scrolling, etc. Create an account on Github and publish your projects, share with the community and get their feedback. Once you have enough projects under your belt, you can start applying for jobs and showcase your projects.

Can Turnitin Detect Chat Gpt?

Turnitin, an effective plagiarism-detection tool, has been quick to respond to the increasing worries in world of education that AI writing tools like ChatGPT could replace critical thinking. Fortunately, Turnitin is now able to detect whether content is generated by Artificial Intelligence or not; so the question arises – can it detect ChatGPT?

Does Turnitin Detect Chat GPT?

Yes, Turnitin have technology that can detect AI-assisted writing and AI writing generated by tools such as ChatGPT. Turnitin is a plagiarism checker that can properly analyze content to ensure the originality of the work submitted.

Universities and educators can now easily check students’ work and the originality of the content. Turnitin’s ChatGPT Screening can inspect all the content written through an AI chatbot.

The groundbreaking plagiarism checker will be able to detect content’s originality from scratch. Apart from identifying AI written content, Turnitin also provides a report identifying the occurrence of plagiarism.

Schools and universities will highly benefit from Turnitin, as nowadays, students use AI chatbots to complete their assignments and college essays, eliminating the originality, authenticity, and true intention of assignments.

Quick Facts

Yes, Turnitin’s detection runs deeper than just ChatGPT. While they do not specify, other AI text-generating tools are detectable under the latest upgrades for the service.

It provides a Similarity index and originality report to support its identification.

Educators and lecturers can use this AI-writing detector to check students’ work.

It is hard to imagine that ChatGPT launched a few months ago, and Google Bard just a few short weeks. On 4 April 2023, Turnitin launched the AI writing detection capabilities in Turnitin Feedback Studio (TFS), TFS with Originality, Turnitin Originality, Turnitin Similarity, Simcheck, Originality Check, and Originality Check+. The Turnitin Ai detector will support over 2.1 million educators and more than 10,700 institutions, reaching more than 62 million students.

How does Turnitin work?

Lecturers or educators can submit a Turnitin assignment through the university’s online learning environment or upload it directly on Turntitin’s official site. Then students are asked to submit their respective assignments online and upload them as per the given due date.

Once the assignment is submitted by students, Turnitin will examine and analyze the entire submitted work. This plagiarism tool will identify each and every sentence and match it with other sources to detect the originality of the content within a few minutes.

There are two things provided by Turnitin each for submitted work:

Similarity Index: Turnitin’s software takes the content submitted and compares it with a wide range of databases of content, including academic, internet, and student paper content. Then turnitin starts looking for similarities and reports them with a percentage. This includes the percentage of matched or plagiarized content analyzed by Turnitin with other sources.

The similarity index will properly indicate matched content in the submitted paper. Students and educators then check what sentences or phrases are matching and what changes need to be made to avoid further similarity issues.

Originality Report: It will provide a complete report of matched content in extra detail, including Turnitin’s sources. It can include articles, blogs, websites, books, and more which has been previously submitted.

With Turnitin software available, it will be way easier for universities and professors to detect AI-written content within a few minutes. Now, students can work on preparing original assignments and essays rather than asking chatbots to generate content and solve queries for them.

Turnitin ChatGPT Screening

Availability of AI chatbots like ChatGPT has made it easier for students to generate content within a few seconds. Users can raise a request for essays, articles, blogs, etc, and ChatGPT will generate in a human-like tone.

Many students use ChatGPT for their assignments, homework, and college essays. This has led to many serious issues, such as copying, and inauthentic content, which removes the purpose of universities and colleges giving assignments and homework.

AI chatbots’ ability to produce content and responses in a human tone has made it difficult for schools and universities to distinguish between AI-written and human-written content. To avoid this issue, AI content detectors like Turnitin were introduced. Turnitin ChatGPT Screening can identify and analyze the originality of the content provided. It can analyze and detect content from scratch.

Why Is This Significant?

AI detection tools like Turnitin are significant to ensure students are not using AI chatbots to complete their assignments, essays, and homework. Using AI chatbots like ChatGPT to complete the entire paper on the student’s behalf is unethical and can be considered entirely plagiarized. Students need to understand AI chatbots can’t be used as a replacement to complete the academic work assigned to students to help enhance their skills, knowledge, and critical thinking.

How Can Instructors Detect the Use of Chat GPT?

Apart from using AI-detecting tools like Turnitin, instructors can detect the usage of ChatGPT in students’ work by analyzing the style, tone, and language used in the writing. ChatGPT or any other AI chatbot tends to have a particular style used by the bot.

Additionally, chatbots also tend to use certain words or phrases that students usually do not use, which can help instructors detect the original writer behind the assignment submitted. Instructors can even ask students to explain the ideas or concepts of their assignment in their own words to identify whether the student has understood the work.

Can teachers tell if you use ChatGPT?

Yes, AI-detecting tools can help teachers determine whether your submitted content was human-written or AI-generated. ChatGPT utilizes an AI engine that contains a combination of GPT-3.5 language model and AI detecting tools like Turnitin and compares your submitted content with a wide range of databases, articles, blogs, and more.

Besides this, AI chatbots contain a specific language, tone, and style that tends to stand out and distinguish from human writing. This helps the teacher tell if you are using a ChatGPT or not.

The Ethical Implications of Using Chat GPT

Using ChatGPT for school assignments, homework, and papers can be helpful for students as it reduces their workload. However, using AI chatbots is extremely unethical as the AI system is completing the work assigned to the students for their knowledge, skills, and growth.

Nowadays, students are using ChatGPT for the completion of their assignments, university essays, papers, and more. This can be considered a form of cheating and can make shortcuts in writing or even display inaccurate information, which can later cause trouble for the students. Therefore, it’s essential for students to understand these ethical implications before utilizing ChatGPT.

FAQ’s

What does Turnitin not detect?

Turnitin doesn’t detect Plagiarism. Instead, Turnitin provides a similarity report in which the software takes in the student’s submitted work and compares it with the database of the content, including various student paper content, internet, academic, and more, and looks for similarity percentages, not plagiarism.

What gets flagged on Turnitin?

Algorithms of Turnitin AI detecting systems identify the document thoroughly and if they witness any inconsistency in the document which sets it apart from a regular submission or caught something strange, Turnitin will flag it for you to review.

What can I exclude from Turnitin?

In Turnitin, you can exclude any bibliography or any area within the document that contains content or quotes which have been used by the author.

Can you outsmart Turnitin?

You can outsmart Turnitin by rewriting the ChatGPT-generated content in your words, changing the sentence structure, using synonyms, using a personal story or examples in the content, and more. This will help make your content authentic and outsmart AI-detecting tools like Turnitin.

How do you avoid similarity on Turnitin?

To avoid similarity on Turnitin, you must cite your sources and reference, use quotations, avoid too many quotes, rewrite sentences in your own words, and avoid copying word to word from other sources.

What percentage is unacceptable in Turnitin?

Conclusion

How Can Tensorflow And Pre

Tensorflow and the pre-trained model can be used for feature extraction by setting the ‘trainable’ feature of the previously created ‘base_model’ to ‘False’.

Read More: What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?

A neural network that contains at least one layer is known as a convolutional layer. We can use the Convolutional Neural Network to build learning model.

We will understand how to classify images of cats and dogs with the help of transfer learning from a pre-trained network. The intuition behind transfer learning for image classification is, if a model is trained on a large and general dataset, this model can be used to effectively serve as a generic model for the visual world. It would have learned the feature maps, which means the user won’t have to start from scratch by training a large model on a large dataset.

Read More: How can a customized model be pre-trained?

We are using the Google Colaboratory to run the below code. Google Colab or Colaboratory helps run Python code over the browser and requires zero configuration and free access to GPUs (Graphical Processing Units). Colaboratory has been built on top of Jupyter Notebook.

Example print("Feature extraction") base_model.trainable = False print("The base model architecture") The base model architecture Model: "mobilenetv2_1.00_160" __________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== input_1 (InputLayer) [(None, 160, 160, 3) 0 __________________________________________________________________________________________________ Conv1 (Conv2D) (None, 80, 80, 32) 864 input_1[0][0] __________________________________________________________________________________________________ bn_Conv1 (BatchNormalization) (None, 80, 80, 32) 128 Conv1[0][0] __________________________________________________________________________________________________ Conv1_relu (ReLU) (None, 80, 80, 32) 0 bn_Conv1[0][0] __________________________________________________________________________________________________ expanded_conv_depthwise (Depthw (None, 80, 80, 32) 288 Conv1_relu[0][0] __________________________________________________________________________________________________ expanded_conv_depthwise_BN (Bat (None, 80, 80, 32) 128 expanded_conv_depthwise[0][0] __________________________________________________________________________________________________ expanded_conv_depthwise_relu (R (None, 80, 80, 32) 0 expanded_conv_depthwise_BN[0][0] __________________________________________________________________________________________________ expanded_conv_project (Conv2D) (None, 80, 80, 16) 512 expanded_conv_depthwise_relu[0][0 __________________________________________________________________________________________________ expanded_conv_project_BN (Batch (None, 80, 80, 16) 64 expanded_conv_project[0][0] __________________________________________________________________________________________________ block_1_expand (Conv2D) (None, 80, 80, 96) 1536 expanded_conv_project_BN[0][0] __________________________________________________________________________________________________ block_1_expand_BN (BatchNormali (None, 80, 80, 96) 384 block_1_expand[0][0] __________________________________________________________________________________________________ block_1_expand_relu (ReLU) (None, 80, 80, 96) 0 block_1_expand_BN[0][0] __________________________________________________________________________________________________ block_1_pad (ZeroPadding2D) (None, 81, 81, 96) 0 block_1_expand_relu[0][0] __________________________________________________________________________________________________ block_1_depthwise (DepthwiseCon (None, 40, 40, 96) 864 block_1_pad[0][0] __________________________________________________________________________________________________ block_1_depthwise_BN (BatchNorm (None, 40, 40, 96) 384 block_1_depthwise[0][0] __________________________________________________________________________________________________ block_1_depthwise_relu (ReLU) (None, 40, 40, 96) 0 block_1_depthwise_BN[0][0] __________________________________________________________________________________________________ block_1_project (Conv2D) (None, 40, 40, 24) 2304 block_1_depthwise_relu[0][0] __________________________________________________________________________________________________ block_1_project_BN (BatchNormal (None, 40, 40, 24) 96 block_1_project[0][0] __________________________________________________________________________________________________ block_2_expand (Conv2D) (None, 40, 40, 144) 3456 block_1_project_BN[0][0] __________________________________________________________________________________________________ block_2_expand_BN (BatchNormali (None, 40, 40, 144) 576 block_2_expand[0][0] __________________________________________________________________________________________________ block_2_expand_relu (ReLU) (None, 40, 40, 144) 0 block_2_expand_BN[0][0] __________________________________________________________________________________________________ block_2_depthwise (DepthwiseCon (None, 40, 40, 144) 1296 block_2_expand_relu[0][0] __________________________________________________________________________________________________ block_2_depthwise_BN (BatchNorm (None, 40, 40, 144) 576 block_2_depthwise[0][0] __________________________________________________________________________________________________ block_2_depthwise_relu (ReLU) (None, 40, 40, 144) 0 block_2_depthwise_BN[0][0] __________________________________________________________________________________________________ block_2_project (Conv2D) (None, 40, 40, 24) 3456 block_2_depthwise_relu[0][0] __________________________________________________________________________________________________ block_2_project_BN (BatchNormal (None, 40, 40, 24) 96 block_2_project[0][0] __________________________________________________________________________________________________ block_2_add (Add) (None, 40, 40, 24) 0 block_1_project_BN[0][0] block_2_project_BN[0][0] __________________________________________________________________________________________________ block_3_expand (Conv2D) (None, 40, 40, 144) 3456 block_2_add[0][0] __________________________________________________________________________________________________ block_3_expand_BN (BatchNormali (None, 40, 40, 144) 576 block_3_expand[0][0] __________________________________________________________________________________________________ block_3_expand_relu (ReLU) (None, 40, 40, 144) 0 block_3_expand_BN[0][0] __________________________________________________________________________________________________ block_3_pad (ZeroPadding2D) (None, 41, 41, 144) 0 block_3_expand_relu[0][0] __________________________________________________________________________________________________ block_3_depthwise (DepthwiseCon (None, 20, 20, 144) 1296 block_3_pad[0][0] __________________________________________________________________________________________________ block_3_depthwise_BN (BatchNorm (None, 20, 20, 144) 576 block_3_depthwise[0][0] __________________________________________________________________________________________________ block_3_depthwise_relu (ReLU) (None, 20, 20, 144) 0 block_3_depthwise_BN[0][0] __________________________________________________________________________________________________ block_3_project (Conv2D) (None, 20, 20, 32) 4608 block_3_depthwise_relu[0][0] __________________________________________________________________________________________________ block_3_project_BN (BatchNormal (None, 20, 20, 32) 128 block_3_project[0][0] __________________________________________________________________________________________________ block_4_expand (Conv2D) (None, 20, 20, 192) 6144 block_3_project_BN[0][0] __________________________________________________________________________________________________ block_4_expand_BN (BatchNormali (None, 20, 20, 192) 768 block_4_expand[0][0] __________________________________________________________________________________________________ block_4_expand_relu (ReLU) (None, 20, 20, 192) 0 block_4_expand_BN[0][0] __________________________________________________________________________________________________ block_4_depthwise (DepthwiseCon (None, 20, 20, 192) 1728 block_4_expand_relu[0][0] __________________________________________________________________________________________________ block_4_depthwise_BN (BatchNorm (None, 20, 20, 192) 768 block_4_depthwise[0][0] __________________________________________________________________________________________________ block_4_depthwise_relu (ReLU) (None, 20, 20, 192) 0 block_4_depthwise_BN[0][0] __________________________________________________________________________________________________ block_4_project (Conv2D) (None, 20, 20, 32) 6144 block_4_depthwise_relu[0][0] __________________________________________________________________________________________________ block_4_project_BN (BatchNormal (None, 20, 20, 32) 128 block_4_project[0][0] __________________________________________________________________________________________________ block_4_add (Add) (None, 20, 20, 32) 0 block_3_project_BN[0][0] block_4_project_BN[0][0] __________________________________________________________________________________________________ block_5_expand (Conv2D) (None, 20, 20, 192) 6144 block_4_add[0][0] __________________________________________________________________________________________________ block_5_expand_BN (BatchNormali (None, 20, 20, 192) 768 block_5_expand[0][0] __________________________________________________________________________________________________ block_5_expand_relu (ReLU) (None, 20, 20, 192) 0 block_5_expand_BN[0][0] __________________________________________________________________________________________________ block_5_depthwise (DepthwiseCon (None, 20, 20, 192) 1728 block_5_expand_relu[0][0] __________________________________________________________________________________________________ block_5_depthwise_BN (BatchNorm (None, 20, 20, 192) 768 block_5_depthwise[0][0] __________________________________________________________________________________________________ block_5_depthwise_relu (ReLU) (None, 20, 20, 192) 0 block_5_depthwise_BN[0][0] __________________________________________________________________________________________________ block_5_project (Conv2D) (None, 20, 20, 32) 6144 block_5_depthwise_relu[0][0] __________________________________________________________________________________________________ block_5_project_BN (BatchNormal (None, 20, 20, 32) 128 block_5_project[0][0] __________________________________________________________________________________________________ block_5_add (Add) (None, 20, 20, 32) 0 block_4_add[0][0] block_5_project_BN[0][0] __________________________________________________________________________________________________ block_6_expand (Conv2D) (None, 20, 20, 192) 6144 block_5_add[0][0] __________________________________________________________________________________________________ block_6_expand_BN (BatchNormali (None, 20, 20, 192) 768 block_6_expand[0][0] __________________________________________________________________________________________________ block_6_expand_relu (ReLU) (None, 20, 20, 192) 0 block_6_expand_BN[0][0] __________________________________________________________________________________________________ block_6_pad (ZeroPadding2D) (None, 21, 21, 192) 0 block_6_expand_relu[0][0] __________________________________________________________________________________________________ block_6_depthwise (DepthwiseCon (None, 10, 10, 192) 1728 block_6_pad[0][0] __________________________________________________________________________________________________ block_6_depthwise_BN (BatchNorm (None, 10, 10, 192) 768 block_6_depthwise[0][0] __________________________________________________________________________________________________ block_6_depthwise_relu (ReLU) (None, 10, 10, 192) 0 block_6_depthwise_BN[0][0] __________________________________________________________________________________________________ block_6_project (Conv2D) (None, 10, 10, 64) 12288 block_6_depthwise_relu[0][0] __________________________________________________________________________________________________ block_6_project_BN (BatchNormal (None, 10, 10, 64) 256 block_6_project[0][0] __________________________________________________________________________________________________ block_7_expand (Conv2D) (None, 10, 10, 384) 24576 block_6_project_BN[0][0] __________________________________________________________________________________________________ block_7_expand_BN (BatchNormali (None, 10, 10, 384) 1536 block_7_expand[0][0] __________________________________________________________________________________________________ block_7_expand_relu (ReLU) (None, 10, 10, 384) 0 block_7_expand_BN[0][0] __________________________________________________________________________________________________ block_7_depthwise (DepthwiseCon (None, 10, 10, 384) 3456 block_7_expand_relu[0][0] __________________________________________________________________________________________________ block_7_depthwise_BN (BatchNorm (None, 10, 10, 384) 1536 block_7_depthwise[0][0] __________________________________________________________________________________________________ block_7_depthwise_relu (ReLU) (None, 10, 10, 384) 0 block_7_depthwise_BN[0][0] __________________________________________________________________________________________________ block_7_project (Conv2D) (None, 10, 10, 64) 24576 block_7_depthwise_relu[0][0] __________________________________________________________________________________________________ block_7_project_BN (BatchNormal (None, 10, 10, 64) 256 block_7_project[0][0] __________________________________________________________________________________________________ block_7_add (Add) (None, 10, 10, 64) 0 block_6_project_BN[0][0] block_7_project_BN[0][0] __________________________________________________________________________________________________ block_8_expand (Conv2D) (None, 10, 10, 384) 24576 block_7_add[0][0] __________________________________________________________________________________________________ block_8_expand_BN (BatchNormali (None, 10, 10, 384) 1536 block_8_expand[0][0] __________________________________________________________________________________________________ block_8_expand_relu (ReLU) (None, 10, 10, 384) 0 block_8_expand_BN[0][0] __________________________________________________________________________________________________ block_8_depthwise (DepthwiseCon (None, 10, 10, 384) 3456 block_8_expand_relu[0][0] __________________________________________________________________________________________________ block_8_depthwise_BN (BatchNorm (None, 10, 10, 384) 1536 block_8_depthwise[0][0] __________________________________________________________________________________________________ block_8_depthwise_relu (ReLU) (None, 10, 10, 384) 0 block_8_depthwise_BN[0][0] __________________________________________________________________________________________________ block_8_project (Conv2D) (None, 10, 10, 64) 24576 block_8_depthwise_relu[0][0] __________________________________________________________________________________________________ block_8_project_BN (BatchNormal (None, 10, 10, 64) 256 block_8_project[0][0] __________________________________________________________________________________________________ block_8_add (Add) (None, 10, 10, 64) 0 block_7_add[0][0] block_8_project_BN[0][0] __________________________________________________________________________________________________ block_9_expand (Conv2D) (None, 10, 10, 384) 24576 block_8_add[0][0] __________________________________________________________________________________________________ block_9_expand_BN (BatchNormali (None, 10, 10, 384) 1536 block_9_expand[0][0] __________________________________________________________________________________________________ block_9_expand_relu (ReLU) (None, 10, 10, 384) 0 block_9_expand_BN[0][0] __________________________________________________________________________________________________ block_9_depthwise (DepthwiseCon (None, 10, 10, 384) 3456 block_9_expand_relu[0][0] __________________________________________________________________________________________________ block_9_depthwise_BN (BatchNorm (None, 10, 10, 384) 1536 block_9_depthwise[0][0] __________________________________________________________________________________________________ block_9_depthwise_relu (ReLU) (None, 10, 10, 384) 0 block_9_depthwise_BN[0][0] __________________________________________________________________________________________________ block_9_project (Conv2D) (None, 10, 10, 64) 24576 block_9_depthwise_relu[0][0] __________________________________________________________________________________________________ block_9_project_BN (BatchNormal (None, 10, 10, 64) 256 block_9_project[0][0] __________________________________________________________________________________________________ block_9_add (Add) (None, 10, 10, 64) 0 block_8_add[0][0] block_9_project_BN[0][0] __________________________________________________________________________________________________ block_10_expand (Conv2D) (None, 10, 10, 384) 24576 block_9_add[0][0] __________________________________________________________________________________________________ block_10_expand_BN (BatchNormal (None, 10, 10, 384) 1536 block_10_expand[0][0] __________________________________________________________________________________________________ block_10_expand_relu (ReLU) (None, 10, 10, 384) 0 block_10_expand_BN[0][0] __________________________________________________________________________________________________ block_10_depthwise (DepthwiseCo (None, 10, 10, 384) 3456 block_10_expand_relu[0][0] __________________________________________________________________________________________________ block_10_depthwise_BN (BatchNor (None, 10, 10, 384) 1536 block_10_depthwise[0][0] __________________________________________________________________________________________________ block_10_depthwise_relu (ReLU) (None, 10, 10, 384) 0 block_10_depthwise_BN[0][0] __________________________________________________________________________________________________ block_10_project (Conv2D) (None, 10, 10, 96) 36864 block_10_depthwise_relu[0][0] __________________________________________________________________________________________________ block_10_project_BN (BatchNorma (None, 10, 10, 96) 384 block_10_project[0][0] __________________________________________________________________________________________________ block_11_expand (Conv2D) (None, 10, 10, 576) 55296 block_10_project_BN[0][0] __________________________________________________________________________________________________ block_11_expand_BN (BatchNormal (None, 10, 10, 576) 2304 block_11_expand[0][0] __________________________________________________________________________________________________ block_11_expand_relu (ReLU) (None, 10, 10, 576) 0 block_11_expand_BN[0][0] __________________________________________________________________________________________________ block_11_depthwise (DepthwiseCo (None, 10, 10, 576) 5184 block_11_expand_relu[0][0] __________________________________________________________________________________________________ block_11_depthwise_BN (BatchNor (None, 10, 10, 576) 2304 block_11_depthwise[0][0] __________________________________________________________________________________________________ block_11_depthwise_relu (ReLU) (None, 10, 10, 576) 0 block_11_depthwise_BN[0][0] __________________________________________________________________________________________________ block_11_project (Conv2D) (None, 10, 10, 96) 55296 block_11_depthwise_relu[0][0] __________________________________________________________________________________________________ block_11_project_BN (BatchNorma (None, 10, 10, 96) 384 block_11_project[0][0] __________________________________________________________________________________________________ block_11_add (Add) (None, 10, 10, 96) 0 block_10_project_BN[0][0] block_11_project_BN[0][0] __________________________________________________________________________________________________ block_12_expand (Conv2D) (None, 10, 10, 576) 55296 block_11_add[0][0] __________________________________________________________________________________________________ block_12_expand_BN (BatchNormal (None, 10, 10, 576) 2304 block_12_expand[0][0] __________________________________________________________________________________________________ block_12_expand_relu (ReLU) (None, 10, 10, 576) 0 block_12_expand_BN[0][0] __________________________________________________________________________________________________ block_12_depthwise (DepthwiseCo (None, 10, 10, 576) 5184 block_12_expand_relu[0][0] __________________________________________________________________________________________________ block_12_depthwise_BN (BatchNor (None, 10, 10, 576) 2304 block_12_depthwise[0][0] __________________________________________________________________________________________________ block_12_depthwise_relu (ReLU) (None, 10, 10, 576) 0 block_12_depthwise_BN[0][0] __________________________________________________________________________________________________ block_12_project (Conv2D) (None, 10, 10, 96) 55296 block_12_depthwise_relu[0][0] __________________________________________________________________________________________________ block_12_project_BN (BatchNorma (None, 10, 10, 96) 384 block_12_project[0][0] __________________________________________________________________________________________________ block_12_add (Add) (None, 10, 10, 96) 0 block_11_add[0][0] block_12_project_BN[0][0] __________________________________________________________________________________________________ block_13_expand (Conv2D) (None, 10, 10, 576) 55296 block_12_add[0][0] __________________________________________________________________________________________________ block_13_expand_BN (BatchNormal (None, 10, 10, 576) 2304 block_13_expand[0][0] __________________________________________________________________________________________________ block_13_expand_relu (ReLU) (None, 10, 10, 576) 0 block_13_expand_BN[0][0] __________________________________________________________________________________________________ block_13_pad (ZeroPadding2D) (None, 11, 11, 576) 0 block_13_expand_relu[0][0] __________________________________________________________________________________________________ block_13_depthwise (DepthwiseCo (None, 5, 5, 576) 5184 block_13_pad[0][0] __________________________________________________________________________________________________ block_13_depthwise_BN (BatchNor (None, 5, 5, 576) 2304 block_13_depthwise[0][0] __________________________________________________________________________________________________ block_13_depthwise_relu (ReLU) (None, 5, 5, 576) 0 block_13_depthwise_BN[0][0] __________________________________________________________________________________________________ block_13_project (Conv2D) (None, 5, 5, 160) 92160 block_13_depthwise_relu[0][0] __________________________________________________________________________________________________ block_13_project_BN (BatchNorma (None, 5, 5, 160) 640 block_13_project[0][0] __________________________________________________________________________________________________ block_14_expand (Conv2D) (None, 5, 5, 960) 153600 block_13_project_BN[0][0] __________________________________________________________________________________________________ block_14_expand_BN (BatchNormal (None, 5, 5, 960) 3840 block_14_expand[0][0] __________________________________________________________________________________________________ block_14_expand_relu (ReLU) (None, 5, 5, 960) 0 block_14_expand_BN[0][0] __________________________________________________________________________________________________ block_14_depthwise (DepthwiseCo (None, 5, 5, 960) 8640 block_14_expand_relu[0][0] __________________________________________________________________________________________________ block_14_depthwise_BN (BatchNor (None, 5, 5, 960) 3840 block_14_depthwise[0][0] __________________________________________________________________________________________________ block_14_depthwise_relu (ReLU) (None, 5, 5, 960) 0 block_14_depthwise_BN[0][0] __________________________________________________________________________________________________ block_14_project (Conv2D) (None, 5, 5, 160) 153600 block_14_depthwise_relu[0][0] __________________________________________________________________________________________________ block_14_project_BN (BatchNorma (None, 5, 5, 160) 640 block_14_project[0][0] __________________________________________________________________________________________________ block_14_add (Add) (None, 5, 5, 160) 0 block_13_project_BN[0][0] block_14_project_BN[0][0] __________________________________________________________________________________________________ block_15_expand (Conv2D) (None, 5, 5, 960) 153600 block_14_add[0][0] __________________________________________________________________________________________________ block_15_expand_BN (BatchNormal (None, 5, 5, 960) 3840 block_15_expand[0][0] __________________________________________________________________________________________________ block_15_expand_relu (ReLU) (None, 5, 5, 960) 0 block_15_expand_BN[0][0] __________________________________________________________________________________________________ block_15_depthwise (DepthwiseCo (None, 5, 5, 960) 8640 block_15_expand_relu[0][0] __________________________________________________________________________________________________ block_15_depthwise_BN (BatchNor (None, 5, 5, 960) 3840 block_15_depthwise[0][0] __________________________________________________________________________________________________ block_15_depthwise_relu (ReLU) (None, 5, 5, 960) 0 block_15_depthwise_BN[0][0] __________________________________________________________________________________________________ block_15_project (Conv2D) (None, 5, 5, 160) 153600 block_15_depthwise_relu[0][0] __________________________________________________________________________________________________ block_15_project_BN (BatchNorma (None, 5, 5, 160) 640 block_15_project[0][0] __________________________________________________________________________________________________ block_15_add (Add) (None, 5, 5, 160) 0 block_14_add[0][0] block_15_project_BN[0][0] __________________________________________________________________________________________________ block_16_expand (Conv2D) (None, 5, 5, 960) 153600 block_15_add[0][0] __________________________________________________________________________________________________ block_16_expand_BN (BatchNormal (None, 5, 5, 960) 3840 block_16_expand[0][0] __________________________________________________________________________________________________ block_16_expand_relu (ReLU) (None, 5, 5, 960) 0 block_16_expand_BN[0][0] __________________________________________________________________________________________________ block_16_depthwise (DepthwiseCo (None, 5, 5, 960) 8640 block_16_expand_relu[0][0] __________________________________________________________________________________________________ block_16_depthwise_BN (BatchNor (None, 5, 5, 960) 3840 block_16_depthwise[0][0] __________________________________________________________________________________________________ block_16_depthwise_relu (ReLU) (None, 5, 5, 960) 0 block_16_depthwise_BN[0][0] __________________________________________________________________________________________________ block_16_project (Conv2D) (None, 5, 5, 320) 307200 block_16_depthwise_relu[0][0] __________________________________________________________________________________________________ block_16_project_BN (BatchNorma (None, 5, 5, 320) 1280 block_16_project[0][0] __________________________________________________________________________________________________ Conv_1 (Conv2D) (None, 5, 5, 1280) 409600 block_16_project_BN[0][0] __________________________________________________________________________________________________ Conv_1_bn (BatchNormalization) (None, 5, 5, 1280) 5120 Conv_1[0][0] __________________________________________________________________________________________________ out_relu (ReLU) (None, 5, 5, 1280) 0 Conv_1_bn[0][0] ================================================================================================== Total params: 2,257,984 Trainable params: 0 Non-trainable params: 2,257,984 _________________________________________________________________________ Explanation

The convolutional base created from the previous step is frozen and used as a feature extractor.

A classifier is added on top of it to train the top-level classifier.

Freezing is done by setting layer.trainable = False.

This step avoids the weights in a layer from getting updated during training.

MobileNet V2 has many layers, hence setting the model’s entire trainable flag to False would freeze all the layers.

When layer.trainable = False, the BatchNormalization layer runs in inference mode, and won’t update mean and variance statistics.

When a model is unfreezed, it contains BatchNormalization layer to do fine-tuning.

This can be done by passing training = False when the base model is called.

Else, the updates applied to non-trainable weights will spoil what the model has learned.

Working From Home Can Ravage Your Spine, But Good Posture Can Help Fix It

While working from home, we tend to throw all sense of workplace decorum out the window. It’s easy to let yourself get into a slump—literally and figuratively. But even in quarantine (especially in quarantine), it’s super important to take care of your body, and posture plays a big role in physical health. So why is it so hard to sit up straight and so easy to slouch?

To explain that, we’ll have to understand what posture is in the first place. Posture generally refers to how you hold your body. Good posture, sometimes called “neutral spine,” is thought to be the optimal position that puts the least amount of stress on your body as you stand, sit, or sleep.

To achieve neutral spine, your head, shoulders, and hips should all be in line vertically when viewed from the side. Your spine should have three natural curves: a slight inward curvature of the neck, the upper back curved gently out, and the lower back curved gently in.

All of your skeletal muscles are involved with posture, but the most important ones are the core stabilizing muscles around your abdomen, pelvis and back. When those muscles are nice and strong, good posture is much easier to maintain. But periods of slouching can weaken those muscles, and wrongly strengthen others, making it much harder to stand tall. Then, when it comes to correcting your posture, it feels like a workout because you’re trying to re-strengthen those muscles you’ve neglected.

“Your body and your muscles are like clay: whatever position you hold them in, they will mold into,” says Rudy Gehrman, a chiropractor and the CEO of Physio Logic, a New York City-based physical wellness center. “If you keep pressing on a little sapling tree in a certain direction, then it’s going to grow in that direction.” So as we lean over our laptops, our muscles morph to fit that shape. And this bad posture is more than a bad habit—it has real physical consequences.

If you recall your high school biology lessons, you’ll remember that your spinal cord is part of your central nervous system. It’s the highway of neurons that connects your body to your brain. Bad posture contorts the spine and adds undue pressure, creating an accumulation of micro-injuries that can affect a person’s health and mood. For example, research has long associated bad posture with sports injuries like ankle injuries or pulled muscles and hamstrings, and also with slower recovery time from those injuries. However, even if you’re just sitting on your couch at home, bad posture can injure you. A 2013 study showed that bad, forward-leaning head posture worsened pre-existing pain, and correlated with more visits to the doctor.

Psychology studies show that people with slumped posture had more negative thoughts, remembered worse memories, and had a harder time recovering from bad moods. There is clear evidence that while people are working from home they’re spending more time online than in pre-pandemic days, but your screen time shouldn’t be a detriment to your health and mood.

Taking steps to correct posture can offer some tantalizing health benefits including reduced chronic pain and an easier time breathing. Plus, good posture can also benefit your mood and mental health. Studies have shown that good posture can boost self-esteem, mitigate and build resilience to stress, and even help alleviate depressive symptoms.

So how do you reap the benefits of good posture? Understanding what good posture is and then consciously correcting yourself on a regular basis is key to returning your spine to its optimal shape.

There’s growing evidence that the autonomic part of our nervous system, which is responsible for handling breathing and heart rate, is also largely responsible for how we hold our bodies. That means posture is likely regulated subconsciously—“except when your mom walks in and tells you, ‘sit up straight,’ then it becomes conscious,” says Gehrman.

Gehrman says one of the worst habits for posture is bringing our bodies to technology rather than bringing technology to us. Most of the products we use were never designed to work for our anatomical health, like how laptops make us lean in and how we hunch over to look at our phones.

Creating a workspace that works for your body and your posture is key to a healthy spine. Arrange your tools in such a way so your body forms right angles when seated—your back is perpendicular to your thighs, but parallel to your shins. Then, lift your head up and don’t lean forward into your screen.

Fixing bad posture will be slow, but worth it. “It takes one to two months of being hyperconscious of posture to change your subconscious posture,” Gehrman says. To reach that two month mark of vigilant posture correction, get your friends and family involved and ask them to monitor your posture, or put little reminders on your phone.

Ultimately, good posture shows confidence and health. Those are good things to feel, especially in quarantine.

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