Shadowing Practice: What is AI and ML? [Pt 2] | Generative AI with .NET for Beginners - Learn English Speaking with Video
Ders oluşturuluyor...
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Hello and welcome back to this beginner series on machine learning and AI.
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In this video, I'm going to give you a quick introduction to what AI and machine learning are.
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In this video, some of the things that you're going to be learning about are, what is AI and ML?
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We're also going to learn about what you can do with AI and ML, and as a .NET developer, how you can get started with AI and ML.
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All right, so first off, what is AI and ML?
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You can look at it as different layers of basically the same thing.
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And at the top level, you have AI or artificial intelligence.
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And these are typically agents that automate tasks that are typically performed by humans.
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So you can think of this as your spam filter in your email, right?
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So at the end of the day, once you set some rules as to how you want your email organized, It's some sort of autonomous agent that's performing these actions on your behalf,
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as opposed to you having to do it by yourself.
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At the next level, you have machine learning or ML.
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And these are essentially systems that use algorithms and data to infer patterns.
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Now, the reason why you have machine learning is, again, think of your email inbox.
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There's a certain point where rules become so overwhelming
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that it's really difficult to sort of make any sort of meaning out of those rules and manage that.
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So that's sort of where machine learning comes in.
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And again, using data and algorithms, it's able to learn what those rules are and infer those patterns.
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A level lower here, you have deep learning.
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It's a machine learning technique that uses specialized algorithms known as neural networks.
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And then last but not least, you have generative AI, such as the one used in models like GPT and ChatGPT.
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And essentially this uses deep learning algorithms, typically known as transformers, to generate new content like text, images, and other content.
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So what can you do with AI and ML?
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At a high level, you can essentially extract key insights and summarize information with AI and ML.
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Some examples of that are language.
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So you could put, for example, analyze the sentiment of customer reviews.
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You can build virtual agents or chatbots, like the one pictured here, Bing Chat.
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You can translate text or code, and you can even use these models to explain code.
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When it comes to computer vision, you can use these models to identify objects in an image or a video.
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Similarly, for classification, if you need to tag customer reported issues with some severity level, you can leverage machine learning for that and AI.
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You can also do anomaly detection, tasks like predictive maintenance, when a certain piece of equipment is going to fail.
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Last but not least, you have forecasting.
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So if you're looking at quarterly sales or some sort of target that you're looking to forecast or number, you can use AI and ML to do that.
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Now, this is not a complete list, but this is just a relatively good overview of the things that you can do with AI and ML.
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So now that you know what AI and ML is and what you can do with it, how can you get started as a.NET developer?
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There's a few ways and we're going to go over a few of these throughout this entire series.
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So first and foremost, you have services such as Azure OpenAI for working with generative AI models,
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and then you have Azure Conduct Services or AI services.
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What these allow you to do is they allow you to perform task-specific tasks and workflows using the power of AI.
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You also have frameworks and libraries like Symantec Kernel for working with generative AI models.
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You have ML.NET for training custom machine learning models.
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You have libraries like AutoML, Torch Sharp, and SciSharp, which help you train custom machine learning and deep learning models.
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You have the Open Neural Network Exchange or ONNX, which helps you when inferencing and bringing models trained in other platforms over to .NET,
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and you have other libraries like plali.net, which help you with graphing and visualizing your data.
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Then last but not least, there's a series of tools to help you with these workflows, such as ModelBuilder and the ML.NET CLI, when it comes to training custom machine learning models or deep learning models.
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Then there's Polyglot Notebooks, which help with that experimentation process, and just writing code in general.
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That brings to the end of this video, and let's recap what we learned here.
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We learned what AI NML is.
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We learned the things that you can do with AI NML.
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And then last but not least, we wrapped it up with how you as a .NET developer can get started building AI applications.
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Thanks for watching.
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See you in the next video.
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Bu dersin kelimeleri ve konuşma notları
Bu videoda gölgeleme çalışması için 52 cümle ve 835 kelime var. Konuşma bölümü 4:19 sürüyor. Konuşmacı hızlı konuşuyor, dakikada yaklaşık 193 kelime; bu yüzden birbirine bağlanan ve zayıflayan sesler duyacaksınız. Kelimelerin %83’i İngilizcede en sık kullanılan 3.000 kelime arasında; geri kalanına başlamadan önce bakmakta fayda var.
Bu videodaki önemli kelimeler
Videodan öğrenmeye değer 15 kelime, telaffuzu ve anlamıyla:
| Kelime | Telaffuz | Anlam |
|---|---|---|
| algorithm isim | /ˈælɡəɹɪðm̩/ | algoritma |
| custom isim | /ˈkʌstəm/ | adet, görenek |
| developer isim | /dɪˈvɛləpɚ/ | geliştirici |
| forecast isim | /ˈfɔːkɑːst/ | tahmin |
| technique isim | /tɛkˈniːk/ | teknik |
| layer isim | /ˈleɪ̯ɚ/ | katman, tabaka |
| virtual sıfat | /ˈvɜɹt͡ʃuəl/ | sanal |
| framework isim | /ˈfɹeɪm.wɜːk/ | çatkı, çatık |
| filter isim | /ˈfɪltɚ/ | filtre |
| artificial sıfat | /ˌɑː.tɪˈfɪʃ.əl/ | yapay, suni |
| classification isim | /ˌklæsɪfɪˈkeɪʃən/ | sınıflandırma |
| overview isim | /ˈoʊ.vɚˌvjuː/ | genel bakış, kısaca gözden geçirme |
| graph isim | /ɡɹæf/ | grafik |
| spam isim | /spæm/ | spam, yığın mesaj |
| extract isim | /ˈɛkstɹækt/ | ekstre |
Dikkat edilecek telaffuzlar
Konuşmacı you're, we're, I'm gibi kısaltılmış biçimleri 6 kez kullanıyor. Bunları duyduğunuz gibi kısa söyleyin.
- “sh” ve “zh” sesleri: essentially /ɪˈsɛnʃəli/, artificial /ˌɑː.tɪˈfɪʃ.əl/, classification /ˌklæsɪfɪˈkeɪʃən/, detection /dɪˈtɛk.ʃən/, experimentation /ɪkˌspɛɹ.ə.mənˈteɪ.ʃən/
- Uzun kelimeler — vurguyu doğru yere koyun: essentially /ɪˈsɛnʃəli/, developer /dɪˈvɛləpɚ/, similarly /ˈsɪm.ə.lə.li/, artificial /ˌɑː.tɪˈfɪʃ.əl/, classification /ˌklæsɪfɪˈkeɪʃən/
Bu videoyla nasıl çalışılır
- Videonun tamamını konuşmadan bir kez dinleyin ve bilmediğiniz kelimeleri not edin.
- 0,75× hızla başlayın, cümle cümle tekrar edin ve kolaylaşınca normal hıza dönün.
- Kendinizi kaydedin ve orijinaliyle karşılaştırın; algorithm, custom, developer gibi kelimelere özellikle dikkat edin.
Gölgeleme Tekniği Nedir?
Gölgeleme, başlangıçta profesyonel tercüman eğitimi için geliştirilen ve çok dilli Dr. Alexander Arguelles tarafından popüler hale getirilen, bilim destekli bir dil öğrenme tekniğidir. Yöntem basit ama güçlüdür: ana dili İngilizce olan bir sesi dinler ve hemen yüksek sesle tekrar edersiniz — konuşmacıyı 1-2 saniye gecikmeyle takip eden bir gölge gibi. Pasif dinleme veya dilbilgisi alıştırmalarının aksine, gölgeleme beyninizi ve ağız kaslarınızı gerçek konuşma kalıplarını eşzamanlı olarak işlemeye ve yeniden üretmeye zorlar. Araştırmalar, telaffuz doğruluğu, tonlama, ritim, bağlı konuşma, dinleme anlama ve konuşma akıcılığını önemli ölçüde geliştirdiğini göstermektedir — bu da onu IELTS Konuşma hazırlığı ve gerçek dünya İngilizce iletişimi için en etkili yöntemlerden biri yapar.