Acerca de este curso: 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 one 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.
Para quién es esta clase: Data Scientists who want to master Deep Learning and AI, Developers who want to get their hands dirty with the most promising technology of this century
Creada por: IBM
Enseñado por: Romeo Kienzler, Chief Data Scientist, Course Lead
IBM Watson IoT
Enseñado por: Niketan Pansare, Senior Software Engineer
IBM Research
Enseñado por: Tom Hanlon, Training Director
Skymind
Enseñado por: Max Pumperla, Deep Learning Engineer
Enseñado por: Ilja Rasin, Data Scientist
IBM Watson Health
| Nivel | Advanced |
| Compromiso | 4 weeks of study, 4-6 hours/week |
| Idioma | English |
| Cómo aprobar | Aprueba todas las tareas calificadas para completar el curso. |
| Calificaciones del usuario |
Programa
SEMANA 1
Introduction to deep learning
19 videos, 5 readings
- Vídeo: A warm welcome from John Cohn, IBM Fellow Watson IoT
- Vídeo: Introduction - Romeo Kienzler
- Vídeo: Introduction - Ilja Rasin
- Vídeo: Introduction - Niketan Pansare
- Vídeo: Introduction - Tom Hanlon
- Vídeo: Course Logistics
- Reading: Registration for the IBM Cloud account (free)
- Vídeo: Cloud Architectures for AI and DeepLearning
- Reading: IMPORTANT READING!!!
- Reading: Step-by-step tutorials for setting up the environment
- Vídeo: Setup the NodeRED Boilerplate
- Vídeo: Setup jupyter and ApacheSpark
- Vídeo: Linear algebra
- Vídeo: Deep feed forward neural networks
- Vídeo: Convolutional Neural Networks
- Vídeo: Recurrent neural networks
- Vídeo: LSTMs
- Vídeo: Auto encoders and representation learning
- Vídeo: Methods for neural network training
- Vídeo: Gradient Descent Updater Strategies
- Vídeo: How to choose the correct activation function
- Vídeo: The bias-variance tradeoff in deep learning
- Reading: Introduction to ApacheSpark (optional)
- Reading: Link to Github
Calificado: Warmup assignment
Calificado: DeepLearning Fundamentals
SEMANA 2
deep learning frameworks
24 videos, 1 reading
- Vídeo: Intoduction to TensorFlow
- Vídeo: Neural Network Debugging with TensorBoard
- Vídeo: Automatic Differentiation
- Vídeo: Introduction video
- Vídeo: Keras overview
- Vídeo: Sequential models in keras
- Vídeo: Feed forward networks
- Vídeo: Recurrent neural networks
- Vídeo: Beyond sequential models: the functional API
- Vídeo: Saving and loading models
- Vídeo: What is SystemML (1/2) ?
- Vídeo: What is SystemML (2/2) ?
- Vídeo: Demo - How to use Apache SystemML on IBM DSX (1/3)
- Vídeo: Demo - How to use Apache SystemML on IBM DSX (2/3)
- Vídeo: Demo - How to use Apache SystemML on IBM DSX (3/3)
- Vídeo: Introduction to DeepLearning4J
- Vídeo: Demo: Running Java in Data Science Experience
- Vídeo: DL4J Neural Network Code Example, Mnist Classifier
- Vídeo: PyTorch Installation
- Vídeo: PyTorch Packages
- Vídeo: Tensor Creation and Visualization of Higher Dimensional Tensors
- Vídeo: Math Computation and Reshape
- Vídeo: Computation Graph, CUDA
- Vídeo: Linear Model
- Reading: Link to files in Github
Calificado: TensorFlow
Calificado: Building and serialising Keras models
Calificado: Apache SystemML
Calificado: DL4J Fundamentals
Calificado: PyTorch Introduction
SEMANA 3
DeepLearning Applications
18 videos, 1 reading
- Vídeo: Introduction to Anomaly Detection
- Vídeo: How to implement an anomaly detector (1/2)
- Vídeo: How to implement an anomaly detector (2/2)
- Vídeo: How to deploy a real-time anomaly detector
- Vídeo: Introduction to Time Series Forecasting
- Vídeo: Stateful vs. Stateless LSTMs
- Vídeo: Batch Size
- Vídeo: Number of Time Steps, Epochs, Training and Validation
- Vídeo: Trainin Set Size
- Vídeo: Input and Output Data Construction
- Vídeo: Designing the LSTM network in Keras
- Vídeo: Anatomy of a LSTM Node
- Vídeo: Number of Parameters
- Vídeo: Training and loading a saved model
- Vídeo: Classifying the MNIST dataset with Convolutional Neural Networks
- Vídeo: Image classification with Imagenet and Resnet50
- Vídeo: Autoencoder - understanding Word2Vec
- Vídeo: Text Classification with Word Embeddings
- Reading: Link to Github
Calificado: Anomaly Detection
Calificado: Anomaly Detection
Calificado: Sequence Classification with Keras LSTM Network
Calificado: Image Classification
Calificado: NLP
SEMANA 4
scaling and deployment
6 videos, 2 readings, 1 practice quiz
- Reading: Parallel Neural Network Training
- Vídeo: Run Keras Models in Parallel on Apache Spark using Apache SystemML
- Vídeo: Creating and Scaling a Keras Model in ApacheSpark using DL4J
- Vídeo: Creating and Scaling a Keras Model in ApacheSpark using DL4J (Demo)
- Practice Quiz: Run a Notebook using Keras and DL4J
- Vídeo: Scale TensorFlow with IBM Watson Machine Learning
- Vídeo: Computer Vision with IBM Watson Visual Recognition
- Vídeo: Text Classification with IBM Watson Natural Language Classifier
- Reading: Link to Github
Calificado: Apache SystemML
Preguntas Frecuentes
Cómo funciona
Coursework
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Help from Your Peers
Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
Certificates
Earn official recognition for your work, and share your success with friends, colleagues, and employers.
Creadores
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.
Tarifa
| Comprar curso | |
|---|---|
| Accede a los materiales del curso | Disponible |
| Accede a los materiales con calificación | Disponible |
| Recibe una calificación final | Disponible |
| Obtén un Certificado de curso para compartir | Disponible |
Calificaciones y revisiones
Useful information course have. There are some challenges.
However, the instructor, Romeo is great!
A real Jedi master!
It was nice
This course presents a good overview of various practical approaches used in nowadays AI.
Coursera brinda acceso universal a la mejor educación del mundo, al asociarse con las mejores universidades y organizaciones, para ofrecer cursos en línea.
© 2018 Coursera Inc. Todos los derechos reservados.
BB
very effective with hands on experience