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.
Este curso forma parte de Certificado profesional de IBM AI Engineering
Deep Neural Networks with PyTorch
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Nivel intermedio
Aprox. 20 horas para completar
Sugerido: 20 hours/week
Inglés (English)
Subtítulos: Inglés (English)
Programa - Qué aprenderás en este curso
5 horas para completar
Tensor and Datasets
5 horas para completar
6 videos (Total 44 minutos), 2 lecturas, 11 cuestionarios
6 videos
1.1 Tensors 1D13m
1.2 Two-Dimensional Tensors9m
Differentiation in PyTorch5m
1.3 Simple Dataset7m
1.5 Dataset4m
2 lecturas
Labs10m
Labs Review & Download10m
5 ejercicios de práctica
1.1 Tensors 1D5m
1.2 Two-Dimensional Tensors5m
1.3 Derivatives in PyTorch5m
Simple Dataset5m
Datasets10m
3 horas para completar
Linear Regression
3 horas para completar
7 videos (Total 35 minutos), 1 lectura, 10 cuestionarios
7 videos
2.1 Linear Regression Training3m
Loss3m
Gradient Descent4m
Cost3m
Linear Regression PyToch5m
PyTorch Linear Regression Training Slope and Bias5m
1 lectura
Labs Review & Download10m
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), 1 lectura, 8 cuestionarios
5 videos
Mini-Batch Gradient Descent3m
Optimization in PyTorch3m
Training, Validation and Test Split4m
Training, Validation and Test Split PyTorch3m
1 lectura
Labs Review & Download10m
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), 1 lectura, 6 cuestionarios
4 videos
Multiple Linear Regression Training2m
Linear Regression Multiple Outputs5m
Multiple Output Linear Regression Training1m
1 lectura
Labs Review & Download10m
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), 1 lectura, 8 cuestionarios
4 videos
5.1 Logistic Regression: Prediction6m
Bernoulli Distribution and Maximum Likelihood Estimation5m
Logistic Regression Cross Entropy Loss10m
1 lectura
5.4 Labs Review & Download10m
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), 1 lectura, 5 cuestionarios
1 lectura
6.4 Labs Review & Download10m
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), 1 lectura, 12 cuestionarios
6 videos
More Hidden Neurons2m
Neural Networks with Multiple Dimensional Input5m
7.4 Multi-Class Neural Networks5m
7.5 Backpropagation5m
7.5 Activation Functions4m
1 lectura
7.7 Labs Review & Download10m
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 MEMar 29, 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 ANMar 6, 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.
por SKNov 16, 2019
Awesome! This course gives me the basic workflow for using machine learning technique in my research! The materials in the form of Jupyter lab really help!
por TGJan 10, 2020
Very intensive course. Could do more training labs. But this is definitely a very dense course. Extremely helpful to get started on ML/Deep Learning.
por JJFeb 19, 2020
It was a LONG course, very packed with info. But, I feel like I certainly learned a lot and have a great foundation for further learning.
por EEJan 19, 2020
Good, thorough course. Does not hold the student to any kind of standard or accountability and quizzes are ridiculously easy to pass.
por AZMar 24, 2020
It is freeaaakin hard if you take the whole IBM AI ENGINEERING Professional Cert in the duration of a trial period.
por PDJan 2, 2020
The course is interesting and well organized but the quiz are not challenging and full of typos.
por PJFeb 12, 2020
More graded coding assignments would have been better, but content is good!
por KBDec 26, 2019
It would have been better if graded programming assignments were there.
por RRDec 8, 2019
Very Clear explanation and rich labs. The quiz can be more challenging
por SWJan 25, 2020
Great course with in depth material & hands-on learning.
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.
¿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.
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