Acerca de este Curso
4.7
163 calificaciones
32 revisiones

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 principiante

Experience in Python coding and high school-level math is required. Prior machine learning or deep learning knowledge is helpful but not required.

Aprox. 6 horas para completar

Sugerido: 4 weeks, 4-5 hours/week...

Inglés (English)

Subtítulos: Inglés (English)

Qué aprenderás

  • Check

    Learn best practices for using TensorFlow, a popular open-source machine learning framework

  • Check

    Build a basic neural network in TensorFlow

  • Check

    Train a neural network for a computer vision application

  • Check

    Understand how to use convolutions to improve your neural network

Habilidades que obtendrás

Computer VisionTensorflowMachine Learning

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 principiante

Experience in Python coding and high school-level math is required. Prior machine learning or deep learning knowledge is helpful but not required.

Aprox. 6 horas para completar

Sugerido: 4 weeks, 4-5 hours/week...

Inglés (English)

Subtítulos: Inglés (English)

Programa - Qué aprenderás en este curso

Semana
1
3 horas para completar

A New Programming Paradigm

Welcome to this course on going from Basics to Mastery of TensorFlow. We're excited you're here! In week 1 you'll get a soft introduction to what Machine Learning and Deep Learning are, and how they offer you a new programming paradigm, giving you a new set of tools to open previously unexplored scenarios. All you need to know is some very basic programming skills, and you'll pick the rest up as you go along. To get started, check out the first video, a conversation between Andrew and Laurence that sets the theme for what you'll study......
4 videos (Total 16 minutos), 5 readings, 3 quizzes
4 videos
A primer in machine learning3m
The ‘Hello World’ of neural networks5m
Working through ‘Hello World’ in TensorFlow and Python3m
5 lecturas
Learner Support10m
From rules to data10m
Try it for yourself10m
Introduction to Google Colaboratory10m
Week 1 Resources10m
1 ejercicio de práctica
Week 1 Quiz
Semana
2
4 horas para completar

Introduction to Computer Vision

Welcome to week 2 of the course! In week 1 you learned all about how Machine Learning and Deep Learning is a new programming paradigm. This week you’re going to take that to the next level by beginning to solve problems of computer vision with just a few lines of code! Check out this conversation between Laurence and Andrew where they discuss it and introduce you to Computer Vision! ...
7 videos (Total 15 minutos), 6 readings, 3 quizzes
7 videos
An Introduction to computer vision2m
Writing code to load training data2m
Coding a Computer Vision Neural Network2m
Walk through a Notebook for computer vision3m
Using Callbacks to control training1m
Walk through a notebook with Callbacks1m
6 lecturas
Exploring how to use data10m
The structure of Fashion MNIST data10m
See how it's done10m
Get hands-on with computer visions
See how to implement Callbacks10m
Week 2 Resources10m
1 ejercicio de práctica
Week 2 Quiz
Semana
3
5 horas para completar

Enhancing Vision with Convolutional Neural Networks

Welcome to week 3! In week 2 you saw a basic Neural Network for Computer Vision. It did the job nicely, but it was a little naive in its approach. This week we’ll see how to make it better, as discussed by Laurence and Andrew here. ...
6 videos (Total 19 minutos), 6 readings, 3 quizzes
6 videos
What are convolutions and pooling?2m
Implementing convolutional layers1m
Implementing pooling layers4m
Improving the Fashion classifier with convolutions4m
Walking through convolutions3m
6 lecturas
Coding convolutions and pooling layers10m
Learn more about convolutions10m
Getting hands-on, your first ConvNet10m
Try it for yourselfs
Experiment with filters and poolss
Week 3 Resources10m
1 ejercicio de práctica
Week 3 Quiz
Semana
4
6 horas para completar

Using Real-world Images

Last week you saw how to improve the results from your deep neural network using convolutions. It was a good start, but the data you used was very basic. What happens when your images are larger, or if the features aren’t always in the same place? Andrew and Laurence discuss this to prepare you for what you’ll learn this week: handling complex images!...
9 videos (Total 27 minutos), 10 readings, 3 quizzes
9 videos
Understanding ImageGenerator4m
Defining a ConvNet to use complex images2m
Training the ConvNet with fit_generator2m
Walking through developing a ConvNet2m
Walking through training the ConvNet with fit_generator3m
Adding automatic validation to test accuracy4m
Exploring the impact of compressing images3m
Outro: A conversation with Andrew1m
10 lecturas
Explore an impactful, real-world solution10m
Designing the neural network10m
Train the ConvNet with ImageGenerator10m
Exploring the solution10m
Training the neural network10m
Experiment with the horse or human classifiers
Get hands-on and use validation30m
Get Hands-on with compacted images30m
Week 4 Resources10m
Outro10m
1 ejercicio de práctica
Week 4 Quiz
4.7
32 revisionesChevron Right

Principales revisiones

por ASMar 9th 2019

Good intro course, but google colab assignments need to be improved. And submitting a jupyter notebook was much more easier, why would I want to login to my google account to be a part of this course?

por CGMar 9th 2019

without a doubt, Laurence's teaching is much better than reading the documentation! The course is a great starting point!

Instructor

Avatar

Laurence Moroney

AI Advocate
Google Brain

Acerca de deeplearning.ai

deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders....

Preguntas Frecuentes

  • Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.

  • Cuando compras un Certificado, obtienes acceso a todos los materiales del curso, incluidas las tareas calificadas. Una vez que completes el curso, se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes participar del curso como oyente sin costo.

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