About this Course

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Learner Career Outcomes

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started a new career after completing these courses

38%

got a tangible career benefit from this course

57%

got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Advanced Level
Approx. 24 hours to complete
English

Skills you will gain

Machine LearningDeep LearningLong Short-Term Memory (ISTM)Apache Spark

Learner Career Outcomes

23%

started a new career after completing these courses

38%

got a tangible career benefit from this course

57%

got a pay increase or promotion
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Advanced Level
Approx. 24 hours to complete
English

Offered by

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IBM

Syllabus - What you will learn from this course

Content RatingThumbs Up85%(3,217 ratings)Info
Week
1

Week 1

5 hours to complete

Introduction to deep learning

5 hours to complete
16 videos (Total 61 min), 4 readings, 2 quizzes
16 videos
Introduction - Romeo Kienzler30s
Introduction - Ilja Rasin1m
Introduction - Niketan Pansare30s
Course Logistics1m
Cloud Architectures for AI and DeepLearning2m
Linear algebra6m
Deep feed forward neural networks12m
Convolutional Neural Networks4m
Recurrent neural networks1m
LSTMs3m
Auto encoders and representation learning2m
Methods for neural network training8m
Gradient Descent Updater Strategies6m
How to choose the correct activation function3m
The bias-variance tradeoff in deep learning3m
4 readings
IBM Digital Badge10m
Video summary on environment setup10m
Where to get all the code and slides for download?10m
Link to Github10m
1 practice exercise
DeepLearning Fundamentals30m
Week
2

Week 2

7 hours to complete

DeepLearning Frameworks

7 hours to complete
18 videos (Total 116 min), 1 reading, 5 quizzes
18 videos
Neural Network Debugging with TensorBoard7m
Automatic Differentiation2m
Introduction video44s
Keras overview5m
Sequential models in keras6m
Feed forward networks7m
Recurrent neural networks9m
Beyond sequential models: the functional API3m
Saving and loading models2m
What is SystemML (1/2)3m
What is SystemML (2/2)6m
PyTorch Installation2m
PyTorch Packages2m
Tensor Creation and Visualization of Higher Dimensional Tensors6m
Math Computation and Reshape7m
Computation Graph, CUDA17m
Linear Model17m
1 reading
Link to files in Github10m
4 practice exercises
TensorFlow30m
TensorFlow 2.x12m
Apache SystemML12m
PyTorch Introduction30m
Week
3

Week 3

7 hours to complete

DeepLearning Applications

7 hours to complete
18 videos (Total 115 min)
18 videos
How to implement an anomaly detector (1/2)11m
How to implement an anomaly detector (2/2)2m
How to deploy a real-time anomaly detector2m
Introduction to Time Series Forecasting4m
Stateful vs. Stateless LSTMs6m
Batch Size5m
Number of Time Steps, Epochs, Training and Validation8m
Trainin Set Size4m
Input and Output Data Construction7m
Designing the LSTM network in Keras10m
Anatomy of a LSTM Node12m
Number of Parameters7m
Training and loading a saved model4m
Classifying the MNIST dataset with Convolutional Neural Networks5m
Image classification with Imagenet and Resnet503m
Autoencoder - understanding Word2Vec8m
Text Classification with Word Embeddings4m
4 practice exercises
Anomaly Detection30m
Sequence Classification with Keras LSTM Network30m
Image Classification30m
NLP30m
Week
4

Week 4

4 hours to complete

Scaling and Deployment

4 hours to complete
3 videos (Total 9 min), 2 readings, 2 quizzes
3 videos
Computer Vision with IBM Watson Visual Recognition2m
Text Classification with IBM Watson Natural Language Classifier1m
2 readings
Exercise: Scale a Deep Learning Model on IBM Watson Machine Learning10m
Link to Github10m
1 practice exercise
Methods of parallel neural network training6m

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About the Advanced Data Science with IBM Specialization

Advanced Data Science with IBM

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