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.
This course is part of the IBM AI Engineering Professional Certificate
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About this Course
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Intermediate Level
Approx. 30 hours to complete
English
Subtitles: English
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 4 of 6 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 30 hours to complete
English
Subtitles: English
Offered by
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.
Syllabus - What you will learn from this course
5 hours to complete
Tensor and Datasets
5 hours to complete
6 videos (Total 44 min), 1 reading, 11 quizzes
6 videos
1.1 Tensors 1D13m
1.2 Two-Dimensional Tensors9m
Differentiation in PyTorch5m
1.3 Simple Dataset7m
1.5 Dataset4m
1 reading
Labs10m
5 practice exercises
1.1 Tensors 1D5m
1.2 Two-Dimensional Tensors5m
1.3 Derivatives in PyTorch5m
Simple Dataset5m
Datasets10m
2 hours to complete
Linear Regression
2 hours to complete
7 videos (Total 35 min)
7 videos
2.1 Linear Regression Training3m
Loss3m
Gradient Descent4m
Cost3m
Linear Regression PyToch5m
PyTorch Linear Regression Training Slope and Bias5m
7 practice exercises
Prediction in One Dimension5m
Linear Regression Training5m
Loss5m
Gradient Descent5m
Cost5m
Training Parameters in PyTorch5m
PyTorch Linear Regression Training Slope and Bias5m
3 hours to complete
Linear Regression PyTorch Way
3 hours to complete
5 videos (Total 21 min)
5 videos
Mini-Batch Gradient Descent3m
Optimization in PyTorch3m
Training, Validation and Test Split4m
Training, Validation and Test Split PyTorch3m
4 practice exercises
Quiz: Stochastic Gradient Descent5m
Mini-Batch Gradient Descent5m
3.3 Optimization in PyTorch5m
Training and Validation Data PyTorch5m
2 hours to complete
Multiple Input Output Linear Regression
2 hours to complete
4 videos (Total 18 min)
4 videos
Multiple Linear Regression Training2m
Linear Regression Multiple Outputs5m
Multiple Output Linear Regression Training1m
2 practice exercises
Multiple Linear Regression Prediction5m
Multiple Output Linear Regression5m
2 hours to complete
Logistic Regression for Classification
2 hours to complete
4 videos (Total 31 min)
4 videos
5.1 Logistic Regression: Prediction6m
Bernoulli Distribution and Maximum Likelihood Estimation5m
Logistic Regression Cross Entropy Loss10m
5 practice exercises
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 hours to complete
Softmax Rergresstion
2 hours to complete
3 videos (Total 18 min)
3 practice exercises
6.1 Softmax Function:Using Lines to Classify Data5m
6.2 Softmax Prediction5m
6.3 Softmax PyTorch Quizz5m
3 hours to complete
Shallow Neural Networks
3 hours to complete
6 videos (Total 33 min)
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 practice exercises
Neural Networks5m
More Hidden Neurons 5m
Neural Networks with Multiple Dimensional Inputs5m
Multi-Class Neural Networks5m
Backpropagation5m
Activation Functions5m
Reviews
TOP REVIEWS FROM DEEP NEURAL NETWORKS WITH PYTORCH
by SYApr 29, 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!!
by RAMay 15, 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.
by 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.
by 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.
About the IBM AI Engineering Professional Certificate
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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