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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Aprox. 31 horas para completar
Inglés (English)
Subtítulos: Inglés (English), Español (Spanish), Ruso (Russian), Francés (French)
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 1 de 1 en
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel intermedio
Aprox. 31 horas para completar
Inglés (English)
Subtítulos: Inglés (English), Español (Spanish), Ruso (Russian), Francés (French)
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)
6 videos
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 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
Reseñas
Principales reseñas 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 KD20 de jul. de 2020
The right level of detail so that you can dive in. I wish there had been a week to cover RNNs as well though, in particular the best way to handle variable length sequences for RNNs :)
por SS26 de jul. de 2020
Wonderful course!!! Best among all the courses under AI Engineer Certificate by IBM. Deep learning always haunted me with the maths involved but now I get a very good start with this.
Acerca de Certificado profesional de IBM AI Engineering
Artificial intelligence (AI) is revolutionizing entire industries, changing the way companies across sectors leverage data to make decisions. To stay competitive, organizations need qualified AI engineers who use cutting-edge methods like machine learning algorithms and deep learning neural networks to provide data driven actionable intelligence for their businesses. This 6-course Professional Certificate is designed to equip you with the tools you need to succeed in your career as an AI or ML engineer.
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