IBM - Applied AI with DeepLearning
4.0 /5- Offered byCoursera
Applied AI with DeepLearning at Coursera Overview
Duration | 24 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Advanced |
Official Website | Explore Free Course  |
Credential | Certificate |
Applied AI with DeepLearning at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 3 of 4 in the Advanced Data Science with IBM Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Advanced Level
- Approx. 24 hours to complete
- English Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Applied AI with DeepLearning at Coursera Course details
- >>> By enrolling in this course you agree to the End User License Agreement as set out in the FAQ. Once enrolled you can access the license in the Resources area <<<
- This course, Applied Artificial Intelligence with DeepLearning, is part of the IBM Advanced Data Science Certificate which IBM is currently creating and gives you easy access to the invaluable insights into Deep Learning models used by experts in Natural Language Processing, Computer Vision, Time Series Analysis, and many other disciplines. We?ll learn about the fundamentals of Linear Algebra and Neural Networks. Then we introduce the most popular DeepLearning Frameworks like Keras, TensorFlow, PyTorch, DeepLearning4J and Apache SystemML. Keras and TensorFlow are making up the greatest portion of this course. We learn about Anomaly Detection, Time Series Forecasting, Image Recognition and Natural Language Processing by building up models using Keras on real-life examples from IoT (Internet of Things), Financial Marked Data, Literature or Image Databases. Finally, we learn how to scale those artificial brains using Kubernetes, Apache Spark and GPUs.
- IMPORTANT: THIS COURSE ALONE IS NOT SUFFICIENT TO OBTAIN THE "IBM Watson IoT Certified Data Scientist certificate". You need to take three other courses where two of them are currently built. The Specialization will be ready late spring, early summer 2018
- Using these approaches, no matter what your skill levels in topics you would like to master, you can change your thinking and change your life. If you?re already an expert, this peep under the mental hood will give your ideas for turbocharging successful creation and deployment of DeepLearning models. If you?re struggling, you?ll see a structured treasure trove of practical techniques that walk you through what you need to do to get on track. If you?ve ever wanted to become better at anything, this course will help serve as your guide.
- Prerequisites: Some coding skills are necessary. Preferably python, but any other programming language will do fine. Also some basic understanding of math (linear algebra) is a plus, but we will cover that part in the first week as well.
- If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging.
Applied AI with DeepLearning at Coursera Curriculum
Introduction to deep learning
A warm welcome from John Cohn, IBM Fellow Watson IoT
Introduction - Romeo Kienzler
Introduction - Ilja Rasin
Introduction - Niketan Pansare
Course Logistics
Cloud Architectures for AI and DeepLearning
Linear algebra
Deep feed forward neural networks
Convolutional Neural Networks
Recurrent neural networks
LSTMs
Auto encoders and representation learning
Methods for neural network training
Gradient Descent Updater Strategies
How to choose the correct activation function
The bias-variance tradeoff in deep learning
IBM Digital Badge
Video summary on environment setup
Where to get all the code and slides for download?
Link to Github
DeepLearning Fundamentals
DeepLearning Frameworks
Intoduction to TensorFlow
Neural Network Debugging with TensorBoard
Automatic Differentiation
Introduction video
Keras overview
Sequential models in keras
Feed forward networks
Recurrent neural networks
Beyond sequential models: the functional API
Saving and loading models
What is SystemML (1/2)
What is SystemML (2/2)
PyTorch Installation
PyTorch Packages
Tensor Creation and Visualization of Higher Dimensional Tensors
Math Computation and Reshape
Computation Graph, CUDA
Linear Model
Link to files in Github
TensorFlow
TensorFlow 2.x
Apache SystemML
PyTorch Introduction
DeepLearning Applications
Introduction to Anomaly Detection
How to implement an anomaly detector (1/2)
How to implement an anomaly detector (2/2)
How to deploy a real-time anomaly detector
Introduction to Time Series Forecasting
Stateful vs. Stateless LSTMs
Batch Size
Number of Time Steps, Epochs, Training and Validation
Trainin Set Size
Input and Output Data Construction
Designing the LSTM network in Keras
Anatomy of a LSTM Node
Number of Parameters
Training and loading a saved model
Classifying the MNIST dataset with Convolutional Neural Networks
Image classification with Imagenet and Resnet50
Autoencoder - understanding Word2Vec
Text Classification with Word Embeddings
Anomaly Detection
Sequence Classification with Keras LSTM Network
Image Classification
NLP
Scaling and Deployment
Run Keras Models in Parallel on Apache Spark using Apache SystemML
Computer Vision with IBM Watson Visual Recognition
Text Classification with IBM Watson Natural Language Classifier
Exercise: Scale a Deep Learning Model on IBM Watson Machine Learning
Link to Github
Methods of parallel neural network training
Applied AI with DeepLearning at Coursera Admission Process
Important Dates
Other courses offered by Coursera
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