Informações sobre o curso
64,828 visualizações recentes

100% online

Comece imediatamente e aprenda em seu próprio cronograma.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Nível avançado

Aprox. 19 horas para completar

Sugerido: 4 weeks of study, 4-6 hours/week...

Inglês

Legendas: Inglês

Habilidades que você terá

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

100% online

Comece imediatamente e aprenda em seu próprio cronograma.

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Nível avançado

Aprox. 19 horas para completar

Sugerido: 4 weeks of study, 4-6 hours/week...

Inglês

Legendas: Inglês

Programa - O que você aprenderá com este curso

Semana
1
5 horas para concluir

Introduction to deep learning

...
17 vídeos ((Total 65 mín.)), 5 leituras, 2 testes
17 videos
Introduction - Romeo Kienzler30s
Introduction - Ilja Rasin1min
Introduction - Niketan Pansare30s
Introduction - Tom Hanlon1min
Course Logistics1min
Cloud Architectures for AI and DeepLearning4min
Linear algebra6min
Deep feed forward neural networks12min
Convolutional Neural Networks4min
Recurrent neural networks1min
LSTMs3min
Auto encoders and representation learning2min
Methods for neural network training8min
Gradient Descent Updater Strategies6min
How to choose the correct activation function3min
The bias-variance tradeoff in deep learning3min
5 leituras
IBM Digital Badge10min
Video summary on environment setup10min
Where to get all the code and slides for download?10min
Introduction to ApacheSpark10min
Link to Github10min
1 exercício prático
DeepLearning Fundamentals14min
Semana
2
7 horas para concluir

deep learning frameworks

...
24 vídeos ((Total 168 mín.)), 1 leitura, 5 testes
24 videos
Neural Network Debugging with TensorBoard7min
Automatic Differentiation2min
Introduction video44s
Keras overview5min
Sequential models in keras6min
Feed forward networks7min
Recurrent neural networks9min
Beyond sequential models: the functional API3min
Saving and loading models2min
What is SystemML (1/2) ?3min
What is SystemML (2/2) ?6min
Demo - How to use Apache SystemML on IBM DSX (1/3)4min
Demo - How to use Apache SystemML on IBM DSX (2/3)3min
Demo - How to use Apache SystemML on IBM DSX (3/3)8min
Introduction to DeepLearning4J12min
Demo: Running Java in Data Science Experience8min
DL4J Neural Network Code Example, Mnist Classifier14min
PyTorch Installation2min
PyTorch Packages2min
Tensor Creation and Visualization of Higher Dimensional Tensors6min
Math Computation and Reshape7min
Computation Graph, CUDA17min
Linear Model17min
1 leituras
Link to files in Github10min
4 exercícios práticos
TensorFlow12min
Apache SystemML12min
DL4J Fundamentals12min
PyTorch Introduction12min
Semana
3
6 horas para concluir

DeepLearning Applications

...
18 vídeos ((Total 115 mín.)), 2 leituras, 5 testes
18 videos
How to implement an anomaly detector (1/2)11min
How to implement an anomaly detector (2/2)2min
How to deploy a real-time anomaly detector2min
Introduction to Time Series Forecasting4min
Stateful vs. Stateless LSTMs6min
Batch Size5min
Number of Time Steps, Epochs, Training and Validation8min
Trainin Set Size4min
Input and Output Data Construction7min
Designing the LSTM network in Keras10min
Anatomy of a LSTM Node12min
Number of Parameters7min
Training and loading a saved model4min
Classifying the MNIST dataset with Convolutional Neural Networks5min
Image classification with Imagenet and Resnet503min
Autoencoder - understanding Word2Vec8min
Text Classification with Word Embeddings4min
2 leituras
Generative Adversarial Networks (GANs) (optional)10min
Generative Adversarial Networks (GANs) (optional)10min
4 exercícios práticos
Anomaly Detection12min
Sequence Classification with Keras LSTM Network12min
Image Classification6min
NLP6min
Semana
4
4 horas para concluir

scaling and deployment

...
5 vídeos ((Total 40 mín.)), 3 leituras, 2 testes
5 videos
Creating and Scaling a Keras Model in ApacheSpark using DL4J14min
Creating and Scaling a Keras Model in ApacheSpark using DL4J (Demo)16min
Computer Vision with IBM Watson Visual Recognition2min
Text Classification with IBM Watson Natural Language Classifier1min
3 leituras
Parallel Neural Network Training10min
Scale a Keras Model with IBM Watson Machine Learning10min
Link to Github10min
1 exercício prático
Run a Notebook using Keras and DL4J6min
4.4
55 avaliaçõesChevron Right

18%

comecei uma nova carreira após concluir estes cursos

29%

consegui um benefício significativo de carreira com este curso

33%

recebi um aumento ou promoção

Principais avaliações do Applied AI with DeepLearning

por RCApr 26th 2018

It was really great learning with coursera and I loved the course. The way faculty teaches here is just awesome as they are very much clear and helped a lot while learning this coursea

por QLApr 9th 2019

This is an excellent course in teaching me not only the deep learning principles but also practical usecases and various frameworks.

Instrutores

Avatar

Romeo Kienzler

Chief Data Scientist, Course Lead
IBM Watson IoT
Avatar

Niketan Pansare

Senior Software Engineer
IBM Research
Avatar

Tom Hanlon

Training Director
Skymind
Avatar

Max Pumperla

Deep Learning Engineer
Avatar

Ilja Rasin

Data Scientist
IBM Watson Health

Sobre 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....

Sobre o Programa de cursos integrados Advanced Data Science with IBM

As a coursera certified specialization completer you will have a proven deep understanding on massive parallel data processing, data exploration and visualization, and advanced machine learning & deep learning. You'll understand the mathematical foundations behind all machine learning & deep learning algorithms. You can apply knowledge in practical use cases, justify architectural decisions, understand the characteristics of different algorithms, frameworks & technologies & how they impact model performance & scalability. If you choose to take this specialization and earn the Coursera specialization certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging....
Advanced Data Science with IBM

Perguntas Frequentes – FAQ

  • Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.

  • Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.

  • The IBM Watson IoT Certified Data Scientist degree is a Coursera specialization IBM is currently creating. This course is one part of 3-4 courses coming up the next couple of months

    Currently only this and another course exist. The other one is the following:

    https://www.coursera.org/learn/exploring-visualizing-iot-data

    The course above will be modified and renamed to "Fundamentals of Applied DataScience" - but if you pass it today, it counts towards the certificate as well

Mais dúvidas? Visite o Central de Ajuda ao Aprendiz.