deeplearning.ai Courses

AI、機械学習、ディープラーニングのための TensorFlow 入門

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Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely Freeソフトウェア開発者であれば、拡張性のあるAI搭載アルゴリズムを構築したい場合、構築ツールの使い方を理解する必要があります。この講座は今後学んでいく「TensorFlow in Practice 専門講座」の一部であり、機械学習用の人気のオープンソースフレームワークであるTensorFlowのベストプラクティスを学習します。 アンドリュー・エンの「 The Machine Learning（機械学習）」と「Deep Learning Specialization（ディープラーニング専門講座）」では、機械学習とディープラーニングの最も重要かつ基本的な原理を学習します。deeplearning.aiが提供する新しい「TensorFlow in Practice 専門講座」では、TensorFlowを使用してそれらの原理を実装し、拡張性のあるモデルを構築して現実世界の問題に適用する方法を学びます。ニューラルネットワークの仕組みについての理解を深めるには、「ディープラーニング専門講座」を受講することをお勧めします。

Natural Language Processing in TensorFlow

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Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeIf you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.In Course 3 of the deeplearning.ai TensorFlow Specialization, you will build natural language processing systems using TensorFlow. You will learn to process text, including tokenizing and representing sentences as vectors, so that they can be input to a neural network. You’ll also learn to apply RNNs, GRUs, and LSTMs in TensorFlow. Finally, you’ll get to train anLSTM on existing text to create original poetry!The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.

Neuronale Netze und Deep Learning

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Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeWenn auch Sie topaktuelle KI für sich nutzen möchten, sind Sie mit diesem Kurs auf dem richtigen Weg. Deep Learning-Pioniere sind vielgefragt und wenn Sie Deep Learning einmal gemeistert haben, stehen Ihnen zahlreiche Karrieremöglichkeiten offen. Deep Learning ist eine neue „Superkraft“, mit der Sie KI-Systeme entwickeln können, die so vor ein paar Jahren gar nicht möglich gewesen wären. Mit diesem Kurs eignen Sie sich die grundlegenden Kenntnisse zu Deep Learning an. Am Ende des Kurses werden Sie die folgenden Fähigkeiten erlangt haben:– Verständnis der wesentlichen Techniktrends, die Deep Learning vorantreiben– Erstellen, Trainieren und Anwenden lückenloser, tiefer neuronaler Netze– Wissen, wie Sie effiziente (vektorisierte) neuronale Netze implementieren– Verständnis der wichtigsten Parameter in der Architektur eines neuronalen NetzesIn diesem Kurs erfahren Sie zudem, wie Deep Learning eigentlich funktioniert, da das Konzept hier nicht nur flüchtig oder oberflächlich beschrieben wird Nach Abschluss des Kurses werden Sie in der Lage sein, Deep Learning für Ihre eigenen Anwendungen zu nutzen. Wenn Sie eine berufliche Laufbahn im Bereich KI anstreben, werden Sie nach diesem Kurs zudem grundlegende Fragen in einem Bewerbungsgespräch beantworten können.Dies ist der erste Kurs der Deep Learning-Spezialisierung

Custom and Distributed Training with TensorFlow

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Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeIn this course, you will:• Learn about Tensor objects, the fundamental building blocks of TensorFlow, understand the difference between the eager and graph modes in TensorFlow, and learn how to use a TensorFlow tool to calculate gradients.• Build your own custom training loops using GradientTape and TensorFlow Datasets to gain more flexibility and visibility with your model training. • Learn about the benefits of generating code that runs in graph mode, take a peek at what graph code looks like, and practice generating this more efficient code automatically with TensorFlow’s tools.• Harness the power of distributed training to process more data and train larger models, faster, get an overview of various distributed training strategies, and practice working with a strategy that trains on multiple GPU cores, and another that trains on multiple TPU cores.The DeepLearning.AI TensorFlow: Advanced Techniques Specialization introduces the features of TensorFlow that provide learners with more control over their model architecture and tools that help them create and train advanced ML models.This Specialization is for early and mid-career software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models.

Build Better Generative Adversarial Networks (GANs)

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Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeIn this course, you will:- Assess the challenges of evaluating GANs and compare different generative models- Use the Fréchet Inception Distance (FID) method to evaluate the fidelity and diversity of GANs- Identify sources of bias and the ways to detect it in GANs- Learn and implement the techniques associated with the state-of-the-art StyleGANsThe DeepLearning.AI Generative Adversarial Networks (GANs) Specialization provides an exciting introduction to image generation with GANs, charting a path from foundational concepts to advanced techniques through an easy-to-understand approach. It also covers social implications, including bias in ML and the ways to detect it, privacy preservation, and more.Build a comprehensive knowledge base and gain hands-on experience in GANs. Train your own model using PyTorch, use it to create images, and evaluate a variety of advanced GANs. This Specialization provides an accessible pathway for all levels of learners looking to break into the GANs space or apply GANs to their own projects, even without prior familiarity with advanced math and machine learning research.

Data Pipelines with TensorFlow Data Services

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Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeBringing a machine learning model into the real world involves a lot more than just modeling. This Specialization will teach you how to navigate various deployment scenarios and use data more effectively to train your model.In this third course, you will:- Perform streamlined ETL tasks using TensorFlow Data Services- Load different datasets and custom feature vectors using TensorFlow Hub and TensorFlow Data Services APIs- Create and use pre-built pipelines for generating highly reproducible I/O pipelines for any dataset- Optimize data pipelines that become a bottleneck in the training process- Publish your own datasets to the TensorFlow Hub library and share standardized data with researchers and developers around the worldThis Specialization builds upon our TensorFlow in Practice Specialization. If you are new to TensorFlow, we recommend that you take the TensorFlow in Practice Specialization first. To develop a deeper, foundational understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.

Practical Data Science

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Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeDevelopment environments might not have the exact requirements as production environments. Moving data science and machine learning projects from idea to production requires state-of-the-art skills. You need to architect and implement your projects for scale and operational efficiency. Data science is an interdisciplinary field that combines domain knowledge with mathematics, statistics, data visualization, and programming skills.The Practical Data Science Specialization brings together these disciplines using purpose-built ML tools in the AWS cloud. It helps you develop the practical skills to effectively deploy your data science projects and overcome challenges at each step of the ML workflow using Amazon SageMaker.This Specialization is designed for data-focused developers, scientists, and analysts familiar with the Python and SQL programming languages who want to learn how to build, train, and deploy scalable, end-to-end ML pipelines - both automated and human-in-the-loop - in the AWS cloud.Each of the 10 weeks features a comprehensive lab developed specifically for this Specialization that provides hands-on experience with state-of-the-art algorithms for natural language processing (NLP) and natural language understanding (NLU), including BERT and FastText using Amazon SageMaker.

Deep Learning

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Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeThe Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology.In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more.AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career. Along the way, you will also get career advice from deep learning experts from industry and academia.

Сверточные нейронные сети

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Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeЭтот курс научит вас строить сверточные нейронные сети и использовать их для обработки изображений. Благодаря глубокому обучению машинное зрение сегодня работает намного лучше, чем всего лишь два года назад, и это позволяет использовать его в самых разных отраслях, начиная от безопасного автономного вождения и точного распознавания лиц и заканчивая автоматической интерпретацией рентгеновских снимков. В рамках курса вы:— научитесь строить сверточные нейронные сети, включая их самые современные виды, такие как остаточные сети;— узнаете, как применять сверточные сети в задачах визуального обнаружения объектов и распознавания изображений;— узнаете, как использовать нейронную передачу стиля для создания изображений;— научитесь применять алгоритмы к изображениям, видео и другим 2D- и 3D-данным.Это четвертый курс специализации «Глубокое обучение».

시퀀스 모델

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Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely Free딥 러닝 전문화의 다섯 번째 과정에서는 시퀀스 모델과 음성 인식, 음악 합성, 챗봇, 기계 번역, 자연어 처리(NLP) 등과 같은 흥미로운 애플리케이션에 익숙해질 것입니다. 이 과정을 이수하면 순환 신경망(RNN)과 GRU 및 LSTM과 같이 일반적으로 사용되는 변형을 구축 및 훈련하고, RNN을 문자 수준의 언어 모델링에 적용하며, 자연어 처리 및 단어 임베딩에 대한 경험을 얻을 수 있으며, HuggingFace 토크나이저 및 트랜스포머 모델을 사용하여 NER 및 질문에 답하기 같은 다양한 NLP 작업을 해결합니다.딥 러닝 전문화 과정은 딥 러닝의 기능, 과제 및 결과를 이해하고 최첨단 AI 기술의 개발에 참여할 준비를 하는 데 도움이 되는 기본 프로그램입니다. 경력을 쌓기 위한 지식과 기술을 습득할 수 있도록 도와줌으로써 AI 세계에서 최종적인 단계를 맡을 수 있는 길을 제공합니다.

Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

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Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeIf you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.

الشبكات العصبونية الالتفافية

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Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely Freeسيُعلمك هذا المساق طريقة إعداد الشبكات العصبونية الالتفافية وتطبيقها على بيانات الصورة. وبفضل التعلم المتعمق، تعمل الرؤية الحاسوبية على نحوٍ أفضل كثيرًا مما كانت عليه في العامين السابقين وأتاح هذا الكثير من التطبيقات الحالية التي تمتد بدءًا من التحكم الذاتي الآمن حتى التعرف على الوجه بصورةٍ دقيقة وحتى القراءة الأوتوماتيكية للصور الإشعاعية. ستُصبح مُلِم بطريقة إعداد شبكة عصبونية التفافية، بما في ذلك التغييرات التي طرأت في الآونة الأخيرة من قبيل الشبكات المتبقية. وستتعرف على طريقة تطبيق الشبكات الالتفافية على وظائف الكشف والتعرف المرئيين. وستتعرف على طريقة استعمال خاصية نقل النمط العصبي لإنتاج الأعمال الفنية. وسيُصبح بوسعك تطبيق هذه الخوارزميات على مجموعة مختلفة من الصور ومقاطع الفيديو وغيرها من المعطيات ثنائية الأبعاد وثلاثية الأبعاد. ويُمثل هذا المساق الرابع من نوعه في تخصص التعلم المتعمق.

Réseaux neuronaux et Deep Learning

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Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeVous souhaitez vous lancer dans l’IA de pointe ? Ce cours est là pour vous y aider. Les ingénieurs en Deep Learning sont très convoités et la maîtrise de ce domaine vous ouvrira de nombreuses opportunités professionnelles. Le Deep Learning est également un nouveau « superpouvoir » qui vous permettra de développer des systèmes d’IA qui n’étaient même pas envisageables il y a encore quelques années.Vous découvrirez dans ce cours les bases du Deep Learning. Une fois que vous l’aurez terminé, vous serez en mesure de :- comprendre les grandes tendances technologiques sur lesquelles repose le Deep Learning ;- développer, entraîner et utiliser des réseaux neuronaux profonds entièrement connectés ;- mettre en œuvre des réseaux neuronaux efficaces (vectorisés) ;- comprendre les principaux paramètres de l’architecture d’un réseau neuronal.Ce cours ne se limitera pas à une description rapide ou superficielle du Deep Learning, mais vous expliquera également son fonctionnement. Une fois que vous l’aurez terminé, vous serez donc en mesure de l’utiliser dans vos propres applications. En outre, si vous recherchez un poste dans l’IA, vous aurez la capacité de répondre à des questions de base posées lors d’entretiens.Il s’agit du premier cours de la Spécialisation Deep Learning.

Повышение эффективности глубоких нейросетей

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Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeЭтот курс научит вас «магии» повышения эффективности глубокого обучения. Вы изучите сложный механизм работы глубокого обучения, узнаете, какие параметры влияют на его эффективность и сможете систематически получать хорошие результаты. Также вы изучите TensorFlow. По прошествии трех недель вы:— освоите передовые методы созданияприложений для глубокого обучения;— научитесь эффективно использовать распространенные «хитрости» работы с нейросетями, включая инициализацию, L2-регуляризацию и регуляризацию методом исключения, пакетную нормализацию и проверку градиента;— научитесь выполнять и применять различные алгоритмы оптимизации, такие как мини-пакетный градиентный спуск, моменты, RMSprop и Adam, а также проверять их сходимость;— освоите передовые методы составления наборов данных для обучения, разработки и тестирования, а также анализа предвзятости и отклонений;— сможете реализовывать нейронную сеть в TensorFlow. Это второй курс специализации «Глубокое обучение».

AI For Medical Treatment

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Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeAI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. This Specialization will give you practical experience in applying machine learning to concrete problems in medicine.Medical treatment may impact patients differently based on their existing health conditions. In this third course, you’ll recommend treatments more suited to individual patients using data from randomized control trials. In the second week, you’ll apply machine learning interpretation methods to explain the decision-making of complex machine learning models. Finally, you’ll use natural language entity extraction and question-answering methods to automate the task of labeling medical datasets.These courses go beyond the foundations of deep learning to teach you the nuances in applying AI to medical use cases. If you are new to deep learning or want to get a deeper foundation of how neural networks work, we recommend that you take the Deep Learning Specialization.