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Introduction to Image Generation - Bahasa Indonesia
Google Cloud Training
Google Cloud Training
Kursus ini memperkenalkan model difusi, yaitu kelompok model machine learning yang belakangan ini menunjukkan potensinya dalam ranah pembuatan gambar. Model difusi mengambil inspirasi dari fisika, khususnya termodinamika. Dalam beberapa tahun terakhir, model difusi menjadi populer baik di dunia industri maupun penelitian. Model difusi mendasari banyak alat dan model pembuatan gambar yang canggih di Google Cloud. Kursus ini memperkenalkan Anda pada teori yang melandasi model difusi dan cara melatih serta men-deploy-nya di Vertex AI.
Register for this course and see more details by visiting:
OpenCourser.com/course/09z1z0/introduction
What's inside
Syllabus
Pengantar Pembuatan Gambar
Kursus ini memperkenalkan model difusi, yaitu kelompok model machine learning yang belakangan ini menunjukkan potensinya dalam ranah pembuatan gambar. Model difusi mengambil inspirasi dari fisika, khususnya termodinamika. Dalam beberapa tahun terakhir, model difusi menjadi populer baik di dunia industri maupun penelitian. Model difusi mendasari banyak alat dan model pembuatan gambar yang canggih di Google Cloud. Kursus ini memperkenalkan Anda pada teori yang melandasi model difusi dan cara melatih serta men-deploy-nya di Vertex AI.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops knowledge of diffusion models, which are useful in image generation, a rapidly growing field in both industry and research
Introduces the theory behind diffusion models, which adds depth to the learning experience
Taught by Google Cloud Training, an organization recognized for its expertise in cloud computing and machine learning
Provides hands-on experience with training and deploying diffusion models in Vertex AI, giving learners practical skills
Requires familiarity with machine learning and Python, which may be a barrier for beginners
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Introduction to Image Generation - Bahasa Indonesia with these
activities:
Review key concepts of physics
Show steps
Refreshes your memory on the basic foundations of physics
Browse courses on
Physics
Show steps
-
Go over your notes from previous physics courses
-
Review online resources like Khan Academy or Coursera
-
Solve practice problems to test your understanding
Review Diffusion Models
Show steps
Brush up on the fundamentals of diffusion models to strengthen your understanding of the course material.
Browse courses on
Diffusion Models
Show steps
-
Revisit key concepts of diffusion models, such as denoising score matching and reverse diffusion.
-
Review the mathematical formulations and algorithms underlying diffusion models.
-
Explore different architectures and variants of diffusion models, such as DDPM, DDM, and Score-SDE.
Machine Learning Basics
Show steps
Review basic machine learning concepts to strengthen foundational understanding.
Show steps
13 other activities
Expand to see all activities and additional details
Show all 16 activities
Follow a tutorial on using the Vertex AI platform
Show steps
Provides you with hands-on experience using the platform
Show steps
-
Find a tutorial that is relevant to your interests
-
Follow the instructions in the tutorial
-
Experiment with the platform on your own
Explore Google Cloud Platform's Diffusion Tools
Show steps
Familiarizing yourself with Google Cloud Platform's diffusion tools will equip you with practical resources and techniques to enhance your image generation capabilities.
Browse courses on
Google Cloud Platform
Show steps
-
Follow tutorials provided by Google Cloud Platform on topics such as deploying diffusion models, optimizing training pipelines, and using pre-trained models.
-
Experiment with the provided code samples and explore the documentation to gain hands-on experience with GCP's diffusion tools.
Implement a Diffusion Model from Scratch
Show steps
Solidify your understanding of diffusion models by implementing one from scratch, gaining hands-on experience with their inner workings.
Browse courses on
Diffusion Models
Show steps
-
Choose a specific diffusion model architecture, such as DDPM or DDM.
-
Implement the forward and backward passes of the model.
-
Train the model on an appropriate dataset, such as CIFAR-10 or CelebA.
-
Evaluate the performance of your model and identify areas for improvement.
Practice working with diffusion models
Show steps
Develops your proficiency with diffusion models
Show steps
-
Follow along with the tutorials provided in the course
-
Work through the practice problems at the end of each module
-
Experiment with different parameters in the diffusion model
Vertex AI Deployment Workshop
Show steps
Attend a workshop to gain hands-on experience deploying diffusion models on Vertex AI.
Browse courses on
Machine Learning Deployment
Show steps
-
Register for the workshop
-
Prepare a diffusion model for deployment
-
Attend the workshop and follow along
-
Deploy the model to Vertex AI
Attend a Workshop on Diffusion Models
Show steps
Expand your knowledge of diffusion models by attending a workshop led by industry experts or academic researchers.
Browse courses on
Diffusion Models
Show steps
-
Identify and register for a relevant workshop on diffusion models.
-
Actively participate in the workshop, ask questions, and engage with speakers.
-
Network with other attendees and exchange ideas related to diffusion models.
Generate Diffusion Models with Different Architectures
Show steps
By practicing generating diffusion models with different architectures, you will gain a practical understanding of their underlying mechanisms and how architectural choices impact their performance.
Browse courses on
Diffusion Models
Show steps
-
Choose a suitable diffusion model library, such as Diffusers or OpenAI's diffusion models.
-
Experiment with different architectures, such as U-Nets, ResNets, or Transformers.
-
Train and evaluate the models on various datasets to compare their performance and identify optimal architectures for specific applications.
Generate and Analyze Image Samples
Show steps
Generating and analyzing image samples will help you evaluate the performance of your diffusion models and gain insights into their capabilities.
Browse courses on
Image Generation
Show steps
-
Use your trained diffusion models to generate images from different prompts and datasets.
-
Qualitatively and quantitatively analyze the generated images to assess their realism, diversity, and adherence to the input prompts.
-
Fine-tune your models based on the analysis results to improve their performance.
Develop a Diffusion Model for a Specific Application
Show steps
Apply your knowledge of diffusion models to create a custom model for a specific application, such as generating photorealistic images or enhancing low-resolution videos.
Browse courses on
Diffusion Models
Show steps
-
Identify the specific application and define the desired outcomes.
-
Design and implement a diffusion model tailored to the requirements of the application.
-
Train and evaluate the model on a relevant dataset.
-
Present your findings and demonstrate the capabilities of your model.
Write a blog post about diffusion models
Show steps
Helps you understand and explain the concepts of diffusion models
Show steps
-
Choose a specific aspect of diffusion models to focus on
-
Research the topic thoroughly
-
Write a clear and concise blog post that explains the topic
Diffusion Model Training Exercises
Show steps
Enhance hands-on skills by practicing diffusion model training exercises.
Show steps
-
Set up a training environment
-
Load and prepare training data
-
Train a diffusion model
-
Evaluate model performance
Attend a conference or workshop on diffusion models
Show steps
Connects you with others in the field who are working on diffusion models
Show steps
-
Find a conference or workshop that is relevant to your interests
-
Register for the event and make travel arrangements
-
Attend the sessions and participate in the discussions
Build a diffusion model using the Vertex AI platform
Show steps
Applies your skills to a real-world project
Show steps
-
Choose a specific problem that you want to solve with a diffusion model
-
Gather the necessary data
-
Build and train a diffusion model using the Vertex AI platform
-
Evaluate the performance of your model
Review key concepts of physics
Show steps
Refreshes your memory on the basic foundations of physics
Browse courses on
Physics
Show steps
Show steps
Show steps
- Go over your notes from previous physics courses
- Review online resources like Khan Academy or Coursera
- Solve practice problems to test your understanding
Review Diffusion Models
Show steps
Brush up on the fundamentals of diffusion models to strengthen your understanding of the course material.
Browse courses on
Diffusion Models
Show steps
Show steps
Show steps
- Revisit key concepts of diffusion models, such as denoising score matching and reverse diffusion.
- Review the mathematical formulations and algorithms underlying diffusion models.
- Explore different architectures and variants of diffusion models, such as DDPM, DDM, and Score-SDE.
Machine Learning Basics
Show steps
Review basic machine learning concepts to strengthen foundational understanding.
Show steps
Show steps
Show steps
13 other activities
Expand to see all activities and additional details
Show all 16 activities
Follow a tutorial on using the Vertex AI platform
Show steps
Provides you with hands-on experience using the platform
Show steps
Show steps
Show steps
- Find a tutorial that is relevant to your interests
- Follow the instructions in the tutorial
- Experiment with the platform on your own
Explore Google Cloud Platform's Diffusion Tools
Show steps
Familiarizing yourself with Google Cloud Platform's diffusion tools will equip you with practical resources and techniques to enhance your image generation capabilities.
Browse courses on
Google Cloud Platform
Show steps
Show steps
Show steps
- Follow tutorials provided by Google Cloud Platform on topics such as deploying diffusion models, optimizing training pipelines, and using pre-trained models.
- Experiment with the provided code samples and explore the documentation to gain hands-on experience with GCP's diffusion tools.
Implement a Diffusion Model from Scratch
Show steps
Solidify your understanding of diffusion models by implementing one from scratch, gaining hands-on experience with their inner workings.
Browse courses on
Diffusion Models
Show steps
Show steps
Show steps
- Choose a specific diffusion model architecture, such as DDPM or DDM.
- Implement the forward and backward passes of the model.
- Train the model on an appropriate dataset, such as CIFAR-10 or CelebA.
- Evaluate the performance of your model and identify areas for improvement.
Practice working with diffusion models
Show steps
Develops your proficiency with diffusion models
Show steps
Show steps
Show steps
- Follow along with the tutorials provided in the course
- Work through the practice problems at the end of each module
- Experiment with different parameters in the diffusion model
Vertex AI Deployment Workshop
Show steps
Attend a workshop to gain hands-on experience deploying diffusion models on Vertex AI.
Browse courses on
Machine Learning Deployment
Show steps
Show steps
Show steps
- Register for the workshop
- Prepare a diffusion model for deployment
- Attend the workshop and follow along
- Deploy the model to Vertex AI
Attend a Workshop on Diffusion Models
Show steps
Expand your knowledge of diffusion models by attending a workshop led by industry experts or academic researchers.
Browse courses on
Diffusion Models
Show steps
Show steps
Show steps
- Identify and register for a relevant workshop on diffusion models.
- Actively participate in the workshop, ask questions, and engage with speakers.
- Network with other attendees and exchange ideas related to diffusion models.
Generate Diffusion Models with Different Architectures
Show steps
By practicing generating diffusion models with different architectures, you will gain a practical understanding of their underlying mechanisms and how architectural choices impact their performance.
Browse courses on
Diffusion Models
Show steps
Show steps
Show steps
- Choose a suitable diffusion model library, such as Diffusers or OpenAI's diffusion models.
- Experiment with different architectures, such as U-Nets, ResNets, or Transformers.
- Train and evaluate the models on various datasets to compare their performance and identify optimal architectures for specific applications.
Generate and Analyze Image Samples
Show steps
Generating and analyzing image samples will help you evaluate the performance of your diffusion models and gain insights into their capabilities.
Browse courses on
Image Generation
Show steps
Show steps
Show steps
- Use your trained diffusion models to generate images from different prompts and datasets.
- Qualitatively and quantitatively analyze the generated images to assess their realism, diversity, and adherence to the input prompts.
- Fine-tune your models based on the analysis results to improve their performance.
Develop a Diffusion Model for a Specific Application
Show steps
Apply your knowledge of diffusion models to create a custom model for a specific application, such as generating photorealistic images or enhancing low-resolution videos.
Browse courses on
Diffusion Models
Show steps
Show steps
Show steps
- Identify the specific application and define the desired outcomes.
- Design and implement a diffusion model tailored to the requirements of the application.
- Train and evaluate the model on a relevant dataset.
- Present your findings and demonstrate the capabilities of your model.
Write a blog post about diffusion models
Show steps
Helps you understand and explain the concepts of diffusion models
Show steps
Show steps
Show steps
- Choose a specific aspect of diffusion models to focus on
- Research the topic thoroughly
- Write a clear and concise blog post that explains the topic
Diffusion Model Training Exercises
Show steps
Enhance hands-on skills by practicing diffusion model training exercises.
Show steps
Show steps
Show steps
- Set up a training environment
- Load and prepare training data
- Train a diffusion model
- Evaluate model performance
Attend a conference or workshop on diffusion models
Show steps
Connects you with others in the field who are working on diffusion models
Show steps
Show steps
Show steps
- Find a conference or workshop that is relevant to your interests
- Register for the event and make travel arrangements
- Attend the sessions and participate in the discussions
Build a diffusion model using the Vertex AI platform
Show steps
Applies your skills to a real-world project
Show steps
Show steps
Show steps
- Choose a specific problem that you want to solve with a diffusion model
- Gather the necessary data
- Build and train a diffusion model using the Vertex AI platform
- Evaluate the performance of your model
Career center
Learners who complete Introduction to Image Generation - Bahasa Indonesia will develop knowledge and skills
that may be useful to these careers:
Peneliti
Peneliti
Peneliti menggunakan metode ilmiah untuk mempelajari berbagai topik, termasuk ilmu fisika, biologi, dan ilmu sosial. Kursus Pengantar Pembuatan Gambar ini memperkenalkan model difusi, sekelompok model pembelajaran mesin yang menunjukkan potensi pembuatan gambar. Model difusi mengambil inspirasi dari termodinamika, cabang fisika yang mempelajari energi dan hubungannya dengan materi. Kursus ini juga memperkenalkan Anda pada teori yang mendasari model difusi dan cara melatih serta menyebarkannya di Vertex AI. Kursus ini akan memberi Anda dasar yang kuat dalam konsep dan teknik yang digunakan dalam pembuatan gambar, yang dapat menjadi aset yang berharga bagi seorang Peneliti.
Ilmuwan Data
Ilmuwan Data
Ilmuwan Data menggunakan keterampilan matematika dan statistik untuk menganalisis dan menginterpretasikan data. Kursus Pengantar Pembuatan Gambar ini memberikan pemahaman tentang model difusi, sekelompok model pembelajaran mesin yang digunakan dalam pembuatan gambar. Kursus ini juga membahas teknik pelatihan dan penyebaran model difusi di Vertex AI, platform pembelajaran mesin Google Cloud. Kursus ini akan membantu Ilmuwan Data membangun fondasi yang kuat dalam pembuatan gambar, yang dapat menjadi aset yang berharga dalam peran mereka.
Insinyur Pembelajaran Mesin
Insinyur Pembelajaran Mesin
Insinyur Pembelajaran Mesin merancang dan membangun sistem pembelajaran mesin untuk memecahkan berbagai masalah. Kursus Pengantar Pembuatan Gambar ini memperkenalkan model difusi, sekelompok model pembelajaran mesin yang menunjukkan potensi pembuatan gambar. Kursus ini juga mencakup pelatihan dan penyebaran model difusi di Vertex AI, platform pembelajaran mesin Google Cloud. Kursus ini akan memberi Insinyur Pembelajaran Mesin pemahaman yang kuat tentang konsep dan teknik pembuatan gambar, yang dapat memperdalam keahlian mereka dalam pembelajaran mesin.
Analis Bisnis
Analis Bisnis
Analis Bisnis menggunakan data untuk membantu organisasi membuat keputusan yang lebih baik. Kursus Pengantar Pembuatan Gambar dapat membantu Analis Bisnis memahami tren dan pola dalam data, terutama data visual. Model difusi yang dibahas dalam kursus ini digunakan untuk pembuatan gambar dan dapat membantu Analis Bisnis mengidentifikasi wawasan dalam data yang mungkin tidak terlihat melalui metode analisis tradisional. Kursus ini akan memberi Analis Bisnis keterampilan berharga untuk mengekstrak wawasan yang lebih berharga dari data.
Manajer Produk
Manajer Produk
Manajer Produk merencanakan dan mengembangkan produk yang memenuhi kebutuhan pelanggan. Kursus Pengantar Pembuatan Gambar ini membahas model difusi, sekelompok model pembelajaran mesin yang digunakan dalam pembuatan gambar. Kursus ini juga mencakup pelatihan dan penyebaran model difusi di Vertex AI, platform pembelajaran mesin Google Cloud. Kursus ini akan membantu Manajer Produk memahami bagaimana pembuatan gambar dapat meningkatkan produk mereka dan memberikan solusi yang lebih inovatif dan menarik bagi pelanggan.
Desainer Grafis
Desainer Grafis
Desainer Grafis membuat konten visual seperti logo, brosur, dan situs web. Kursus Pengantar Pembuatan Gambar akan bermanfaat bagi Desainer Grafis karena memperkenalkan model difusi, sekelompok model pembelajaran mesin yang digunakan dalam pembuatan gambar. Kursus ini juga mencakup pelatihan dan penyebaran model difusi di Vertex AI, platform pembelajaran mesin Google Cloud. Kursus ini akan membantu Desainer Grafis mengotomatiskan aspek-aspek tertentu dari pekerjaan mereka dan menghasilkan gambar berkualitas tinggi dengan lebih efisien.
Pengembang Game
Pengembang Game
Pengembang Game merancang dan mengembangkan permainan video. Kursus Pengantar Pembuatan Gambar dapat membantu Pengembang Game menciptakan grafik dan karakter permainan yang realistis. Model difusi yang dibahas dalam kursus ini digunakan untuk pembuatan gambar dan dapat membantu Pengembang Game menghasilkan aset berkualitas tinggi untuk permainan mereka. Kursus ini akan memberikan Pengembang Game keterampilan tambahan yang dapat membantu mereka menciptakan pengalaman bermain game yang lebih imersif.
Arsitek Solusi
Arsitek Solusi
Arsitek Solusi merancang dan mengimplementasikan solusi teknologi untuk organisasi. Kursus Pengantar Pembuatan Gambar akan memberi Arsitek Solusi pemahaman tentang model difusi, sekelompok model pembelajaran mesin yang digunakan dalam pembuatan gambar. Kursus ini juga mencakup pelatihan dan penyebaran model difusi di Vertex AI, platform pembelajaran mesin Google Cloud. Kursus ini akan membantu Arsitek Solusi membuat solusi yang lebih inovatif dan efektif yang memanfaatkan pembuatan gambar.
Konsultan Teknologi
Konsultan Teknologi
Konsultan Teknologi memberikan saran dan panduan kepada organisasi tentang penggunaan teknologi. Kursus Pengantar Pembuatan Gambar dapat membantu Konsultan Teknologi memahami tren dan perkembangan terbaru dalam pembuatan gambar. Model difusi yang dibahas dalam kursus ini digunakan untuk pembuatan gambar dan dapat membantu Konsultan Teknologi memberikan saran yang lebih tepat kepada klien mereka. Kursus ini akan membantu Konsultan Teknologi tetap mengikuti tren teknologi dan memberikan layanan yang lebih berharga kepada klien mereka.
Wartawan Data
Wartawan Data
Wartawan Data menggunakan data untuk menceritakan kisah dan melaporkan berita. Kursus Pengantar Pembuatan Gambar ini akan membantu Wartawan Data memahami cara menggunakan model difusi, sekelompok model pembelajaran mesin yang digunakan dalam pembuatan gambar, untuk membuat visualisasi data yang menarik dan informatif. Kursus ini juga membahas pelatihan dan penyebaran model difusi di Vertex AI, platform pembelajaran mesin Google Cloud. Kursus ini akan memberikan Wartawan Data keterampilan untuk membuat konten yang lebih menarik dan mudah dipahami.
Analis Keuangan
Analis Keuangan
Analis Keuangan menggunakan data keuangan untuk mengevaluasi investasi dan membuat rekomendasi. Kursus Pengantar Pembuatan Gambar dapat membantu Analis Keuangan memahami tren dan pola dalam data keuangan. Model difusi yang dibahas dalam kursus ini digunakan untuk pembuatan gambar dan dapat membantu Analis Keuangan mengidentifikasi peluang dan risiko investasi. Kursus ini akan memberi Analis Keuangan alat tambahan untuk membuat keputusan investasi yang lebih tepat.
Profesional Pemasaran
Profesional Pemasaran
Profesional Pemasaran mengembangkan dan melaksanakan kampanye pemasaran untuk mempromosikan produk dan layanan. Kursus Pengantar Pembuatan Gambar dapat membantu Profesional Pemasaran memahami bagaimana pembuatan gambar dapat digunakan untuk membuat konten pemasaran yang lebih menarik dan efektif. Model difusi yang dibahas dalam kursus ini digunakan untuk pembuatan gambar dan dapat membantu Profesional Pemasaran menghasilkan gambar dan visual yang memikat untuk kampanye mereka. Kursus ini akan memberikan Profesional Pemasaran keterampilan tambahan untuk membuat kampanye pemasaran yang lebih sukses.
Arsitek Data
Arsitek Data
Arsitek Data merancang dan mengelola arsitektur data untuk organisasi. Kursus Pengantar Pembuatan Gambar dapat membantu Arsitek Data memahami cara menggunakan model difusi, sekelompok model pembelajaran mesin yang digunakan dalam pembuatan gambar, untuk membuat visualisasi data yang efektif. Kursus ini juga membahas pelatihan dan penyebaran model difusi di Vertex AI, platform pembelajaran mesin Google Cloud. Kursus ini akan memberikan Arsitek Data keterampilan untuk membuat arsitektur data yang lebih efektif dan mudah dipahami.
Ahli Statistik
Ahli Statistik
Ahli Statistik menggunakan metode statistik untuk menganalisis data dan membuat kesimpulan. Kursus Pengantar Pembuatan Gambar dapat membantu Ahli Statistik memahami bagaimana pembuatan gambar dapat digunakan untuk memvisualisasikan data dan mengidentifikasi tren dan pola. Model difusi yang dibahas dalam kursus ini digunakan untuk pembuatan gambar dan dapat membantu Ahli Statistik membuat visualisasi yang lebih efektif dan mudah dipahami. Kursus ini akan memberi Ahli Statistik alat tambahan untuk menganalisis data dan mengomunikasikan temuan mereka secara lebih efektif.
Insinyur Perangkat Lunak
Insinyur Perangkat Lunak
Insinyur Perangkat Lunak merancang, mengembangkan, dan memelihara aplikasi perangkat lunak. Kursus Pengantar Pembuatan Gambar dapat membantu Insinyur Perangkat Lunak memahami cara menggunakan model difusi, sekelompok model pembelajaran mesin yang digunakan dalam pembuatan gambar, untuk membuat aplikasi yang lebih inovatif dan menarik. Kursus ini juga membahas pelatihan dan penyebaran model difusi di Vertex AI, platform pembelajaran mesin Google Cloud. Kursus ini akan memberikan Insinyur Perangkat Lunak keterampilan tambahan untuk membangun aplikasi yang lebih canggih dan berfitur lengkap.
For more career information including salaries, visit:
OpenCourser.com/course/09z1z0/introduction
Reading list
We've selected 13 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Introduction to Image Generation - Bahasa Indonesia.
Save
Provides a comprehensive overview of generative adversarial networks (GANs), which are a powerful class of generative models that have shown promising results in a wide range of applications. The book covers the theoretical foundations of GANs, their training and inference algorithms, and their applications in various domains.
Covers a wide range of topics, including image classification, object detection, semantic segmentation, and image generation. It comprehensive resource for anyone interested in learning about machine learning for computer vision.
Provides a practical guide to deep learning for computer vision, with a focus on building real-world applications. It covers a wide range of topics, including image classification, object detection, and semantic segmentation.
Save
Provides a comprehensive overview of computer vision algorithms and applications. It covers a wide range of topics, including image formation, image processing, feature extraction, and object recognition.
Provides a comprehensive overview of pattern recognition and machine learning algorithms. It covers a wide range of topics, including supervised and unsupervised learning, dimensionality reduction, and model selection.
Provides a comprehensive overview of probabilistic graphical models (PGMs), which are a powerful tool for representing and reasoning about complex systems. PGMs are widely used in a variety of applications, including image processing, computer vision, and natural language processing.
Provides a comprehensive overview of Bayesian reasoning and machine learning. It covers a wide range of topics, including probability theory, Bayesian inference, and machine learning algorithms.
Provides a practical guide to machine learning using the Python libraries Scikit-Learn, Keras, and TensorFlow. It covers a wide range of topics, including data preprocessing, feature engineering, model training, and model evaluation.
Provides a practical guide to deep learning using the Python library Keras. It covers a wide range of topics, including neural networks, convolutional neural networks, and recurrent neural networks.
Provides a practical guide to natural language processing (NLP) using the Python library NLTK. It covers a wide range of topics, including text preprocessing, tokenization, stemming, and parsing.
Provides a comprehensive overview of speech and language processing. It covers a wide range of topics, including speech recognition, natural language understanding, and computational linguistics.
Provides a comprehensive overview of information theory, inference, and learning algorithms. It covers a wide range of topics, including probability theory, Bayesian inference, and machine learning algorithms.
Provides a comprehensive overview of statistical learning algorithms. It covers a wide range of topics, including linear regression, logistic regression, and support vector machines.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/09z1z0/introduction
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Level
Introductory
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Language
English
Transcript
Bahasa Indonesia
Good to know
Develops knowledge of diffusion models, which are useful in image generation, a rapidly growing field in both industry and research
Introduces the theory behind diffusion models, which adds depth to the learning experience
Taught by Google Cloud Training, an organization recognized for its expertise in cloud computing and machine learning
Provides hands-on experience with training and deploying diffusion models in Vertex AI, giving learners practical skills
Requires familiarity with machine learning and Python, which may be a barrier for beginners
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