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.
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Acerca de este Curso
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Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 4 de 6 en
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel intermedio
Aprox. 30 horas para completar
Inglés (English)
Subtítulos: Inglés (English)
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 4 de 6 en
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel intermedio
Aprox. 30 horas para completar
Inglés (English)
Subtítulos: Inglés (English)
ofrecido por
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.
Programa - Qué aprenderás en este curso
5 horas para completar
Tensor and Datasets
5 horas para completar
6 videos (Total 44 minutos), 1 lectura, 11 cuestionarios
6 videos
1.1 Tensors 1D13m
1.2 Two-Dimensional Tensors9m
Differentiation in PyTorch5m
1.3 Simple Dataset7m
1.5 Dataset4m
1 lectura
Labs10m
5 ejercicios de práctica
1.1 Tensors 1D5m
1.2 Two-Dimensional Tensors5m
1.3 Derivatives in PyTorch5m
Simple Dataset5m
Datasets10m
2 horas para completar
Linear Regression
2 horas para completar
7 videos (Total 35 minutos)
7 videos
2.1 Linear Regression Training3m
Loss3m
Gradient Descent4m
Cost3m
Linear Regression PyToch5m
PyTorch Linear Regression Training Slope and Bias5m
7 ejercicios de práctica
Prediction in One Dimension5m
Linear Regression Training5m
Loss5m
Gradient Descent5m
Cost5m
Training Parameters in PyTorch5m
PyTorch Linear Regression Training Slope and Bias5m
3 horas para completar
Linear Regression PyTorch Way
3 horas para completar
5 videos (Total 21 minutos)
5 videos
Mini-Batch Gradient Descent3m
Optimization in PyTorch3m
Training, Validation and Test Split4m
Training, Validation and Test Split PyTorch3m
4 ejercicios de práctica
Quiz: Stochastic Gradient Descent5m
Mini-Batch Gradient Descent5m
3.3 Optimization in PyTorch5m
Training and Validation Data PyTorch5m
2 horas para completar
Multiple Input Output Linear Regression
2 horas para completar
4 videos (Total 18 minutos)
4 videos
Multiple Linear Regression Training2m
Linear Regression Multiple Outputs5m
Multiple Output Linear Regression Training1m
2 ejercicios de práctica
Multiple Linear Regression Prediction5m
Multiple Output Linear Regression5m
2 horas para completar
Logistic Regression for Classification
2 horas para completar
4 videos (Total 31 minutos)
4 videos
5.1 Logistic Regression: Prediction6m
Bernoulli Distribution and Maximum Likelihood Estimation5m
Logistic Regression Cross Entropy Loss10m
5 ejercicios de práctica
5.0 Linear Classifiers5m
5.0 Linear Classifiers5m
5.1 Logistic Regression: Prediction10m
Bernoulli Distribution and Maximum Likelihood Estimation5m
5.3 Logistic Regression Cross Entropy Loss10m
2 horas para completar
Softmax Rergresstion
2 horas para completar
3 videos (Total 18 minutos)
3 ejercicios de práctica
6.1 Softmax Function:Using Lines to Classify Data5m
6.2 Softmax Prediction5m
6.3 Softmax PyTorch Quizz5m
3 horas para completar
Shallow Neural Networks
3 horas para completar
6 videos (Total 33 minutos)
6 videos
More Hidden Neurons2m
Neural Networks with Multiple Dimensional Input5m
7.4 Multi-Class Neural Networks5m
7.5 Backpropagation5m
7.5 Activation Functions4m
6 ejercicios de práctica
Neural Networks5m
More Hidden Neurons 5m
Neural Networks with Multiple Dimensional Inputs5m
Multi-Class Neural Networks5m
Backpropagation5m
Activation Functions5m
Revisiones
Principales revisiones sobre DEEP NEURAL NETWORKS WITH PYTORCH
por SY29 de abr. de 2020
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!!
por RA15 de may. de 2020
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.
por ME29 de mar. de 2020
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.
por AN6 de mar. de 2020
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.
Acerca de Certificado profesional de IBM AI Engineering
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.
Preguntas Frecuentes
¿Cuándo podré acceder a las lecciones y tareas?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
- The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
- The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
¿Qué recibiré si me suscribo a este Certificado?
Cuando te inscribes en un curso, obtienes acceso a todos los cursos del Certificado y te darán un certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.
¿Cuál es la política de reembolsos?
Si estás suscrito, obtienes una prueba gratis de 7 días, que podrás cancelar cuando desees sin ningún tipo de penalidad. Una vez transcurrido ese tiempo, no realizamos reembolsos. No obstante, puedes cancelar tu suscripción cuando quieras. Consulta nuestra política completa de reembolsos.
¿Recibiré crédito universitario por completar el Curso?
Este Curso no otorga crédito universitario, pero algunas universidades pueden aceptar los Certificados del curso para obtener crédito. Consulta con tu institución para obtener más información. Los Títulos en línea y los Certificados Mastertrack™ de Coursera brindan la oportunidad de obtener créditos universitarios.
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