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 faz parte do Certificado Profissional IBM AI Engineering
oferecido por
Informações sobre o curso
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Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 4 de 6 no
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Aprox. 30 horas para completar
Inglês
Legendas: Inglês
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 4 de 6 no
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Aprox. 30 horas para completar
Inglês
Legendas: Inglês
oferecido 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 - O que você aprenderá com este curso
5 horas para concluir
Tensor and Datasets
5 horas para concluir
6 vídeos (Total 44 mín.), 1 leitura, 11 testes
6 videos
1.1 Tensors 1D13min
1.2 Two-Dimensional Tensors9min
Differentiation in PyTorch5min
1.3 Simple Dataset7min
1.5 Dataset4min
1 leituras
Labs10min
5 exercícios práticos
1.1 Tensors 1D5min
1.2 Two-Dimensional Tensors5min
1.3 Derivatives in PyTorch5min
Simple Dataset5min
Datasets10min
2 horas para concluir
Linear Regression
2 horas para concluir
7 vídeos (Total 35 mín.)
7 videos
2.1 Linear Regression Training3min
Loss3min
Gradient Descent4min
Cost3min
Linear Regression PyToch5min
PyTorch Linear Regression Training Slope and Bias5min
7 exercícios práticos
Prediction in One Dimension5min
Linear Regression Training5min
Loss5min
Gradient Descent5min
Cost5min
Training Parameters in PyTorch5min
PyTorch Linear Regression Training Slope and Bias5min
3 horas para concluir
Linear Regression PyTorch Way
3 horas para concluir
5 vídeos (Total 21 mín.)
5 videos
Mini-Batch Gradient Descent3min
Optimization in PyTorch3min
Training, Validation and Test Split4min
Training, Validation and Test Split PyTorch3min
4 exercícios práticos
Quiz: Stochastic Gradient Descent5min
Mini-Batch Gradient Descent5min
3.3 Optimization in PyTorch5min
Training and Validation Data PyTorch5min
2 horas para concluir
Multiple Input Output Linear Regression
2 horas para concluir
4 vídeos (Total 18 mín.)
4 videos
Multiple Linear Regression Training2min
Linear Regression Multiple Outputs5min
Multiple Output Linear Regression Training1min
2 exercícios práticos
Multiple Linear Regression Prediction5min
Multiple Output Linear Regression5min
2 horas para concluir
Logistic Regression for Classification
2 horas para concluir
4 vídeos (Total 31 mín.)
4 videos
5.1 Logistic Regression: Prediction6min
Bernoulli Distribution and Maximum Likelihood Estimation5min
Logistic Regression Cross Entropy Loss10min
5 exercícios práticos
5.0 Linear Classifiers5min
5.0 Linear Classifiers5min
5.1 Logistic Regression: Prediction10min
Bernoulli Distribution and Maximum Likelihood Estimation5min
5.3 Logistic Regression Cross Entropy Loss10min
2 horas para concluir
Softmax Rergresstion
2 horas para concluir
3 vídeos (Total 18 mín.)
3 exercícios práticos
6.1 Softmax Function:Using Lines to Classify Data5min
6.2 Softmax Prediction5min
6.3 Softmax PyTorch Quizz5min
3 horas para concluir
Shallow Neural Networks
3 horas para concluir
6 vídeos (Total 33 mín.)
6 videos
More Hidden Neurons2min
Neural Networks with Multiple Dimensional Input5min
7.4 Multi-Class Neural Networks5min
7.5 Backpropagation5min
7.5 Activation Functions4min
6 exercícios práticos
Neural Networks5min
More Hidden Neurons 5min
Neural Networks with Multiple Dimensional Inputs5min
Multi-Class Neural Networks5min
Backpropagation5min
Activation Functions5min
Avaliações
Principais avaliações do 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 Mai 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.
Sobre Certificado Profissional 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.
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