Acerca de este Curso

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Resultados profesionales del estudiante

23%

comenzó una nueva carrera después de completar estos cursos

38%

consiguió un beneficio tangible en su carrera profesional gracias a este curso

57%

consiguió un aumento de sueldo o ascenso
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel avanzado
Aprox. 22 horas para completar
Inglés (English)
Subtítulos: Inglés (English)

Habilidades que obtendrás

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

Resultados profesionales del estudiante

23%

comenzó una nueva carrera después de completar estos cursos

38%

consiguió un beneficio tangible en su carrera profesional gracias a este curso

57%

consiguió un aumento de sueldo o ascenso
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel avanzado
Aprox. 22 horas para completar
Inglés (English)
Subtítulos: Inglés (English)

ofrecido por

Logotipo de IBM

IBM

Programa - Qué aprenderás en este curso

Calificación del contenidoThumbs Up84%(2,864 calificaciones)Info
Semana
1

Semana 1

5 horas para completar

Introduction to deep learning

5 horas para completar
16 videos (Total 61 minutos), 4 lecturas, 2 cuestionarios
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 lecturas
IBM Digital Badge10m
Video summary on environment setup10m
Where to get all the code and slides for download?10m
Link to Github10m
1 ejercicio de práctica
DeepLearning Fundamentals14m
Semana
2

Semana 2

7 horas para completar

DeepLearning Frameworks

7 horas para completar
18 videos (Total 116 minutos), 1 lectura, 5 cuestionarios
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 lectura
Link to files in Github10m
4 ejercicios de práctica
TensorFlow12m
TensorFlow 2.x12m
Apache SystemML12m
PyTorch Introduction12m
Semana
3

Semana 3

6 horas para completar

DeepLearning Applications

6 horas para completar
18 videos (Total 115 minutos)
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 ejercicios de práctica
Anomaly Detection12m
Sequence Classification with Keras LSTM Network12m
Image Classification6m
NLP6m
Semana
4

Semana 4

4 horas para completar

Scaling and Deployment

4 horas para completar
3 videos (Total 9 minutos), 2 lecturas, 2 cuestionarios
3 videos
Computer Vision with IBM Watson Visual Recognition2m
Text Classification with IBM Watson Natural Language Classifier1m
2 lecturas
Exercise: Scale a Deep Learning Model on IBM Watson Machine Learning10m
Link to Github10m
1 ejercicio de práctica
Methods of parallel neural network training6m

Revisiones

Principales revisiones sobre APPLIED AI WITH DEEPLEARNING

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Acerca de Programa especializado: 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

Preguntas Frecuentes

  • Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

    • The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.

    • The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

  • 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

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