The course will teach you how to develop deep learning models using Pytorch. The course will start with Pytorch's tensors and Automatic differentiation package. Then each section will cover different models starting off with fundamentals such as Linear Regression, and logistic/softmax regression. Followed by Feedforward deep neural networks, the role of different activation functions, normalization and dropout layers. Then Convolutional Neural Networks and Transfer learning will be covered. Finally, several other Deep learning methods will be covered.
Этот курс входит в специализацию ''Профессиональная сертификация 'IBM AI Engineering''
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Курс 4 из 6 в программе
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Промежуточный уровень
Прибл. 30 часов на выполнение
Английский
Субтитры: Английский
Сертификат, ссылками на который можно делиться с другими людьми
Получите сертификат по завершении
100% онлайн
Начните сейчас и учитесь по собственному графику.
Курс 4 из 6 в программе
Гибкие сроки
Назначьте сроки сдачи в соответствии со своим графиком.
Промежуточный уровень
Прибл. 30 часов на выполнение
Английский
Субтитры: Английский
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IBM
IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame.
Программа курса: что вы изучите
5 ч. на завершение
Tensor and Datasets
5 ч. на завершение
6 видео ((всего 44 мин.)), 1 материал для самостоятельного изучения, 11 тестов
6 видео
1.1 Tensors 1D13мин
1.2 Two-Dimensional Tensors9мин
Differentiation in PyTorch5мин
1.3 Simple Dataset7мин
1.5 Dataset4мин
1 материал для самостоятельного изучения
Labs10мин
5 практических упражнений
1.1 Tensors 1D5мин
1.2 Two-Dimensional Tensors5мин
1.3 Derivatives in PyTorch5мин
Simple Dataset5мин
Datasets10мин
2 ч. на завершение
Linear Regression
2 ч. на завершение
7 видео ((всего 35 мин.))
7 видео
2.1 Linear Regression Training3мин
Loss3мин
Gradient Descent4мин
Cost3мин
Linear Regression PyToch5мин
PyTorch Linear Regression Training Slope and Bias5мин
7 практических упражнений
Prediction in One Dimension5мин
Linear Regression Training5мин
Loss5мин
Gradient Descent5мин
Cost5мин
Training Parameters in PyTorch5мин
PyTorch Linear Regression Training Slope and Bias5мин
3 ч. на завершение
Linear Regression PyTorch Way
3 ч. на завершение
5 видео ((всего 21 мин.))
5 видео
Mini-Batch Gradient Descent3мин
Optimization in PyTorch3мин
Training, Validation and Test Split4мин
Training, Validation and Test Split PyTorch3мин
4 практических упражнения
Quiz: Stochastic Gradient Descent5мин
Mini-Batch Gradient Descent5мин
3.3 Optimization in PyTorch5мин
Training and Validation Data PyTorch5мин
2 ч. на завершение
Multiple Input Output Linear Regression
2 ч. на завершение
4 видео ((всего 18 мин.))
4 видео
Multiple Linear Regression Training2мин
Linear Regression Multiple Outputs5мин
Multiple Output Linear Regression Training1мин
2 практических упражнения
Multiple Linear Regression Prediction5мин
Multiple Output Linear Regression5мин
2 ч. на завершение
Logistic Regression for Classification
2 ч. на завершение
4 видео ((всего 31 мин.))
4 видео
5.1 Logistic Regression: Prediction6мин
Bernoulli Distribution and Maximum Likelihood Estimation5мин
Logistic Regression Cross Entropy Loss10мин
5 практических упражнений
5.0 Linear Classifiers5мин
5.0 Linear Classifiers5мин
5.1 Logistic Regression: Prediction10мин
Bernoulli Distribution and Maximum Likelihood Estimation5мин
5.3 Logistic Regression Cross Entropy Loss10мин
2 ч. на завершение
Softmax Rergresstion
2 ч. на завершение
3 видео ((всего 18 мин.))
3 практических упражнения
6.1 Softmax Function:Using Lines to Classify Data5мин
6.2 Softmax Prediction5мин
6.3 Softmax PyTorch Quizz5мин
3 ч. на завершение
Shallow Neural Networks
3 ч. на завершение
6 видео ((всего 33 мин.))
6 видео
More Hidden Neurons2мин
Neural Networks with Multiple Dimensional Input5мин
7.4 Multi-Class Neural Networks5мин
7.5 Backpropagation5мин
7.5 Activation Functions4мин
6 практических упражнений
Neural Networks5мин
More Hidden Neurons 5мин
Neural Networks with Multiple Dimensional Inputs5мин
Multi-Class Neural Networks5мин
Backpropagation5мин
Activation Functions5мин
Рецензии
Лучшие отзывы о курсе DEEP NEURAL NETWORKS WITH PYTORCH
от партнера SY29 апр. 2020 г.
An extremely good course for anyone starting to build deep learning models. I am very satisfied at the end of this course as i was able to code models easily using pytorch. Definitely recomended!!
от партнера RA15 мая 2020 г.
This is not a bad course at all. One feedback, however, is making the quizzes longer, and adding difficult questions especially concept-based one in the quiz will be more rewarding and valuable.
от партнера ME29 мар. 2020 г.
this course provides a very good and cohesive introduction to Neural Networks. I learned a lot during my journey and I recommend it for anyone interesting in the field.
от партнера AN6 мар. 2020 г.
It was a very informative and interesting lecture. I learn a lot about the details when using PyTorch to build and train a deep neural network. I am so thankful.
Профессиональная сертификация 'IBM AI Engineering': общие сведения
The rapid pace of innovation in Artificial Intelligence (AI) is creating enormous opportunity for transforming entire industries and our very existence. After competing this comprehensive 6 course Professional Certificate, you will get a practical understanding of Machine Learning and Deep Learning.
You will master fundamental concepts of Machine Learning and Deep Learning, including supervised and unsupervised learning. You will utilize popular Machine Learning and Deep Learning libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow applied to industry problems involving object recognition and Computer Vision, image and video processing, text analytics, Natural Language Processing, recommender systems, and other types of classifiers.
You will be able to scale Machine Learning on Big Data using Apache Spark. You will build, train, and deploy different types of Deep Architectures, including Convolutional Networks, Recurrent Networks, and Autoencoders.
By the end of this Professional Certificate, you will have completed several projects showcasing your proficiency in Machine Learning and Deep Learning, and become armed with skills for a career as an AI Engineer.
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