This course is part of the IBM AI Engineering Professional Certificate

Joseph Santarcangelo

Offered By

IBM AI Engineering Professional CertificateIBM

About this Course

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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

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Earn a Certificate upon completion

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Course 4 of 6 in the

IBM AI Engineering

Flexible deadlines

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Intermediate Level

Approx. 31 hours to complete

English

Subtitles: French, Portuguese (Brazilian), Russian, English, Spanish

Instructor

Instructor rating

4.47/5 (71 Ratings)

Joseph Santarcangelo

Ph.D., Data Scientist at IBM

IBM Developer Skills Network

380,853 Learners

5 Courses

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 4 of 6 in the

IBM AI Engineering

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 31 hours to complete

English

Subtitles: French, Portuguese (Brazilian), Russian, English, Spanish

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

Content Rating87%(1,914 ratings)

Week

Week 1

5 hours to complete

Tensor and Datasets

5 hours to complete

6 videos (Total 44 min), 1 reading, 11 quizzes

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

1 reading

Labs10m

5 practice exercises

1.1 Tensors 1D5m

1.2 Two-Dimensional Tensors5m

1.3 Derivatives in PyTorch5m

Simple Dataset5m

Datasets10m

Week

Week 2

2 hours to complete

Linear Regression

2 hours to complete

7 videos (Total 35 min)

7 videos

2.1 Linear Regression Prediction8m

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

Stochastic Gradient Descentaddsub6m

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

Week

Week 3

2 hours to complete

Multiple Input Output Linear Regression

2 hours to complete

4 videos (Total 18 min)

4 videos

Multiple Linear Regression Prediction8m

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

5.0 Linear Classifiers8m

5.1 Logistic Regression: Prediction6m

Bernoulli Distribution and Maximum Likelihood Estimation5m

Logistic Regression Cross Entropy Loss10m

5 practice exercises

5.0 Linear Classifiers5m

5.1 Logistic Regression: Prediction10m

Bernoulli Distribution and Maximum Likelihood Estimation5m

5.3 Logistic Regression Cross Entropy Loss10m

Week

Week 4

2 hours to complete

Softmax Rergresstion

2 hours to complete

3 videos (Total 18 min)

3 videos

6.1 Softmax7m

6.2 Softmax Function:Using Lines to Classify Data3m

Softmax PyTorch6m

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

What's a Neural Network9m

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

About the IBM AI Engineering Professional Certificate

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.

You’ll master fundamental concepts of machine learning and deep learning, including supervised and unsupervised learning, using programming languages like Python. You’ll apply popular machine learning and deep learning libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow to industry problems involving object recognition, computer vision, image and video processing, text analytics, natural language processing (NLP), recommender systems, and other types of classifiers. Through hands-on projects, you’ll gain essential data science skills scaling machine learning algorithms on big data using Apache Spark. You’ll build, train, and deploy different types of deep architectures, including convolutional neural networks, recurrent networks, and autoencoders. In addition to earning a Professional Certificate from Coursera, you will also receive a digital badge from IBM recognizing your proficiency in AI engineering.

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Deep Neural Networks with PyTorch