Certificate in Deep Learning for QA: Foundational Knowledge

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The Certificate in Deep Learning for QA: Foundational Knowledge is a comprehensive course designed to equip learners with essential skills in deep learning, specifically for Quality Assurance. This program emphasizes the importance of deep learning in modern QA, addressing industry demand for professionals with a strong understanding of these concepts.

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ใ“ใฎใ‚ณใƒผใ‚นใซใคใ„ใฆ

Throughout the course, learners will gain hands-on experience with various deep learning techniques, tools, and methodologies, preparing them for careers in an increasingly automated and data-driven world. By earning this certificate, professionals demonstrate their commitment to staying ahead in the field, showcasing their expertise in deep learning and its applications for QA. In summary, this course empowers learners with the foundational knowledge and practical skills necessary for career advancement in deep learning and QA, addressing a critical industry need and positioning graduates as valuable assets in the modern workforce.

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ใฉใ“ใ‹ใ‚‰ใงใ‚‚ๅญฆ็ฟ’

ๅ…ฑๆœ‰ๅฏ่ƒฝใช่จผๆ˜Žๆ›ธ

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ๅพ…ๆฉŸๆœŸ้–“ใชใ—

ใ‚ณใƒผใ‚น่ฉณ็ดฐ

โ€ข Introduction to Deep Learning: Understanding the basics of deep learning, its applications, and benefits.
โ€ข Neural Networks: Exploring the structure and functionality of neural networks, including feedforward and recurrent networks.
โ€ข Convolutional Neural Networks (CNNs): Learning about CNNs, their architecture, and their use in image recognition and computer vision.
โ€ข Deep Learning Frameworks: Getting familiar with popular deep learning frameworks, such as TensorFlow, PyTorch, and Keras.
โ€ข Training and Optimization Techniques: Understanding optimization algorithms, training strategies, and regularization techniques.
โ€ข Transfer Learning and Fine-Tuning: Learning how to leverage pre-trained models, transfer learning, and fine-tuning.
โ€ข Natural Language Processing (NLP) with Deep Learning: Exploring NLP techniques and models based on deep learning.
โ€ข Generative Models and Applications: Delving into generative models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), and their applications.
โ€ข Evaluation Metrics and Best Practices: Measuring performance and adhering to best practices for deep learning models in QA.

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ใ‚ณใƒผใ‚นใ‚’ๅฎŒไบ†ใ™ใ‚‹ใฎใซใฉใ‚Œใใ‚‰ใ„ๆ™‚้–“ใŒใ‹ใ‹ใ‚Šใพใ™ใ‹๏ผŸ

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ใ„ใคใ‚ณใƒผใ‚นใ‚’้–‹ๅง‹ใงใใพใ™ใ‹๏ผŸ

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ใ‚ณใƒผใ‚นๆƒ…ๅ ฑใ‚’ๅ–ๅพ—

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ใ“ใฎใ‚ณใƒผใ‚นใฎๆ”ฏๆ‰•ใ„ใฎใŸใ‚ใซไผš็คพ็”จใฎ่ซ‹ๆฑ‚ๆ›ธใ‚’ใƒชใ‚ฏใ‚จใ‚นใƒˆใ—ใฆใใ ใ•ใ„ใ€‚

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ใ‚ญใƒฃใƒชใ‚ข่จผๆ˜Žๆ›ธใ‚’ๅ–ๅพ—

ใ‚ตใƒณใƒ—ใƒซ่จผๆ˜Žๆ›ธใฎ่ƒŒๆ™ฏ
CERTIFICATE IN DEEP LEARNING FOR QA: FOUNDATIONAL KNOWLEDGE
ใซๆŽˆไธŽใ•ใ‚Œใพใ™
ๅญฆ็ฟ’่€…ๅ
ใงใƒ—ใƒญใ‚ฐใƒฉใƒ ใ‚’ๅฎŒไบ†ใ—ใŸไบบ
London School of International Business (LSIB)
ๆŽˆไธŽๆ—ฅ
05 May 2025
ใƒ–ใƒญใƒƒใ‚ฏใƒใ‚งใƒผใƒณID๏ผš s-1-a-2-m-3-p-4-l-5-e
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