Este curso forma parte de Certificado profesional de IBM AI Engineering

Deep Neural Networks with PyTorch

ofrecido por

Certificado profesional de IBM AI Engineering

IBM

Acerca de este Curso

5,586 vistas recientes

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.
Learning Outcomes:
After completing this course, learners will be able to:
•	explain and apply their knowledge of Deep Neural Networks and related machine learning methods
•	know how to use Python libraries such as PyTorch  for Deep Learning applications 
•	build Deep Neural Networks using PyTorch

Curso 4 de 6 en

IBM AI Engineering

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel intermedio

Aprox. 17 horas para completar

Sugerido: 9 hours/week...

Inglés (English)

Subtítulos: Inglés (English)

Los estudiantes que toman este Professional Certificate son

Data Scientists

Software Engineers

Curso 4 de 6 en

IBM AI Engineering

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel intermedio

Aprox. 17 horas para completar

Sugerido: 9 hours/week...

Inglés (English)

Subtítulos: Inglés (English)

Programa - Qué aprenderás en este curso

Semana

5 horas para completar

Tensor and Datasets

6 videos (Total 44 minutos), 11 cuestionarios

6 videos

1.0 Overview of Tensors3m

1.1 Tensors 1D13m

1.2 Two-Dimensional Tensors9m

Differentiation in PyTorch5m

1.3 Simple dataset7m

1.5 Dataset4m

5 ejercicios de práctica

1.1 Tensors 1D10m

1.2 Two-Dimensional Tensors5m

1.3 Derivatives in PyTorch10m

Simple Dataset10m

Datasets10m

Semana

3 horas para completar

Linear Regression

2 videos (Total 12 minutos), 5 cuestionarios

2 videos

2.1 Linear Regression Prediction8m

2.1 Linear Regression Training3m

2 ejercicios de práctica

Prediction in One Dimension10m

2.2 Linear Regression Training

Semana

6 horas para completar

Linear Regression PyTorch Way

6 cuestionarios

4 horas para completar

Multiple Input Output Linear Regression

4 cuestionarios

Semana

1 hora para completar

Logistic Regression for Classification

7 cuestionarios

4 ejercicios de práctica

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

4 cuestionarios

2 ejercicios de práctica

6.2 Softmax Function:Using Lines to Classify Data

6.3 Softmax PyTorch Quizz

Instructor

Joseph Santarcangelo

Ph.D., Data Scientist at IBM

IBM Developer Skills Network

Acerca de 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....

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?
Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.
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