Volver a Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

estrellas

17,112 calificaciones

•

3,542 reseña

If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.
The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization....

AS

8 de mar. de 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?

RD

13 de ago. de 2019

Great course to get started with building Convolutional Neural Networks in Keras for building Image Classifiers. This is probably the best way to get beginners into Deep Learning for Computer Vision.

Filtrar por:

por Mohammad F

•1 de feb. de 2020

This course gives a high-level overview to tensorflow keras api which is good to begin with but working on complex use cases would be preferred.

por Yonatan N

•10 de ago. de 2019

This course was not focused on learning tensorflow as I had hoped. Instead, it felt like an into to neural networks course using tensorflow.

por Jeroen v H

•3 de jul. de 2019

I missed graded exercises. WIth only the simple multiple-choice questions tests it becomes a try and retry game to score as high as possible,

por Samuel K

•29 de oct. de 2019

Too easy and lacks theoretical explanation, even though there are references and it seems that it lacks the explanations on purpose...

por RAVI P

•15 de jun. de 2020

The quality of assignments should be better. There should be less emphasis on overfitting the data to 99% or 100%

por Apoorv V

•1 de ago. de 2020

Compared to the Deep Learning Specialization, this course falls short in course depth and coverage of material.

por Stavros K G

•10 de mar. de 2019

I know that it is an introduction but I would like more staff .

por Antonio S

•17 de sep. de 2019

I am quite disappointed with this course. First, it should not been called "Introduction to TensorFlow" but "Introduction to Keras", which is a TensorFlows' (TF) API that entails a higher layer of abstraction. Basic data structures, estimators, graphs, etc. are not explained through the course. Second, video lessons are too superficial and lack of content. They remind me to those of the Machine Learning Crash Course from Google. That is, as an opener/introduction for Deep Learning (DL) are fine but they are far from being an essential training tool in DL (unlike the Deep Learning Specialization here in Coursera). Finally, content is too basic. This course requires an intermediate level, so students are supposed to be already familiar with basic DL concepts. I understand that this first course within the specialization is an introduction, but I just begun the next course (Convolutional Neural Networks in TF) and it is more of the same. Laurence is still working on the binary classification problem and only at the end he treats the multi-class problem. Instead, I was expecting to implement CNN models like ResNets, Inception networks, and applications like object detection or face recognition in TF (not in Keras). For me, it is not worth spending time and money for what you learn in this course. The good part is that, because videos are short and exercise are easy, you can finish the whole course in just one week (or less if you are 100% working on it).

por Dragos B

•15 de mar. de 2020

Maybe I had unrealistic expectations following the original 5 courses from deeplearning.ai. I understand the target audience and need for simplification, BUT there are multiple outright wrong statements, that are unacceptable (will list below):

1 `Softmax takes a set of values, and effectively picks the biggest one, so, for example, if the output of the last layer looks like [0.1, 0.1, 0.05, 0.1, 9.5, 0.1, 0.05, 0.05, 0.05], it saves you from fishing through it looking for the biggest value, and turns it into [0,0,0,0,1,0,0,0,0] -- The goal is to save a lot of coding!` - no it doesn't do that, it takes n numbers and gives n numbers which sum to one and respect all original inequalities. and no it doesn't save time, you still need an argmax.

2 in the first course there's a linear regression trying to learn f(x)=2x-1. The course says you can't get it exactly because you don't have enough data. Of course you have enough data, 2 points are enough to describe a line, and that regression has a closed form solution. SGD with fixed LR is the only problem.

3. Immediately after, also first lesson, it says that sometimes loss goes up and that's called overfitting.

Those were just a few...really I understand it doing baby steps for developers without maths background, but I'm not sure this is doing them any favors..I've also showed these to a bunch of my colleagues and we were on the same page about it

por Aladdin P

•4 de ago. de 2020

I am not very satisfied with the course. It does feel quite professionally made, but there is no depth. It feels as if the teachers of the course had some difficulty when deciding on the prerequisites. I think it would have been clearer if the course would just have said: take this course after the deep learning specialization because this will build on knowledge from previous courses. Then, focus ONLY on teaching the coding part, explain what is TF, what is Keras, in DEPTH. For example, all the quizzes are more theorethical questions: these should be ALL code in TensorFlow. E.g, what is the following code doing? I guess it's just the first course, so depth is not expected but what I've read so far, it wont change in the following courses. I'm dissapointed and Andrew you should set a higher standard for your courses. Hope you will take lessons and not let this happen in future courses, I wasted my money and time on this.

por abdallahDrwesh

•20 de jun. de 2020

No deep details for functions used

por Xiaotian Z

•25 de nov. de 2020

This series of courses is just a 'Hello World' introduction of Tensorflow/Keras. The instructor just touches the surface of some code from the Tensorflow document without explaining some really fundamental concepts (e.g. tensors). The videos are usually 1-2 min long, really a headache to watch. The quiz is too simple and poorly designed-- instead of thinking or calculating you just need to remember some basic concepts/grammar rules. Programming exercises are not really useful and there is too much duplicate work. Not worth the money if you plan to pay for it-- auditing is enough. I am disappointed by deeplearning.ai for producing such a shallow course.

por Walter H L P

•6 de ago. de 2019

Code and exercises look like they were made in a hurry, with a lot of errors that have not been addressed yet, even after been reported about 3 months ago. No challenging practical exercise (just need to copy the code from the previous notebook that the instructor supplied) (maybe making the function print "Reached X% accuracy so cancelling training!" was necessary to fool the grader). Weak theoretical test. I had high expectations, and now I am disappointed with this deeplearning.ai course. I do not recommend, TensorFlow guide have better material to learn about it.

por Mehdi S

•9 de nov. de 2019

I don't actually get the purpose of this course: teaching deep learning or teaching deep learning with TF? Can there be anything else? If the former is the aim, one needs to learn how a deep learning algorithm works and why it is successful. If the goal is teaching TF for people who are familiar with deep learning, first the structure and logic behind TF and then the coding parts should be taught line by line with details.

This course, in my point of vies, has nothing to present.

por Ankit S

•12 de ago. de 2019

This is was the worst course I have ever taken on Coursera and my sample size for courses is statistically significant. a) The grader is not good. b) The infrastructure was not good. c) To complete the course I have to copy the code to Google colab, run there and then copy-paste the code back. This course was very very basic and from an industrial standpoint, it was way below expectation.

por Mark P

•14 de abr. de 2020

Far far too easy. As a big fan of the deeplearning specialisation I was very disappointed in this course. I don't know what they think the learner is supposed to come away with from this course. If this was all the course a person took they really wouldn't know very much at all

por Siddhanth D

•13 de oct. de 2019

What a crap professor. Really wish Andrew Ng taught this course instead. I have no clue what this teacher is talking about he makes 2-3 min videos of complicated material and blabbers about it while referring us to online videos and other resources instead of just explaining it.

por Mohammadreza M

•19 de oct. de 2020

The course is very superficial and rarely add something to your knowledge. Assignments are simple and do not teach you how to use TF in your projects.

por Stephen F

•7 de jun. de 2019

I mistakenly bought this course , Note 43 euro is for this one simple module, be aware please!!

por Ahmad F

•6 de may. de 2020

If you're starting out as a beginner AI practitioner, this is a very good introductory course. The prerequisites for going through the classes are really low. You just have to know basic python and the basic mechanics of deep neural networks beforehand. After completing this course, you'll be very proficient at modelling neural networks to classify images with very high accuracy using tensorflow keras.

This course also explains briefly how to import data of your choice to your neural network to train on, which I think is very cool. It also teaches you about convolutional neural networks, which is what the top industry experts use to do their AI jobs. The exercises in this course are well made, they help you really understand the concepts by making you code them by yourself. All in all, this is a very good introductory course, and Andy Morone is an amazing teacher.

por Frank Z

•15 de jul. de 2020

This course is very friendly to beginners starting to get to know TensorFlow. The only skill needed is basic Python programming. Another good point for this course is students don't have to obtain a local machine that could run the TensorFlow. The tasks could be done over Google Co-lab, which is very conveniently friendly. The only shortcut for this course would be the source codes provided are based on TensorFlow 1.X. Right now, the TensorFlow has released 2.X. Since tf V2 has taken out some functions in V1 as well as changes some expression, it would be very inconvenient if you wanna download the code and test or do your work on your local machine.

Overall, this is a course that I would highly recommend as a beginner.

por Edgar C O

•22 de jun. de 2020

As an introductory course to the Tensorflow platform I think it is excellent. As it is mentioned in the title this is not a course where you are going to see in depth what it is behind the algorithms or the theory behind the implementation but it provides extra links to other sources where the interested student can read on their own, which I think is good. It is self-contained, the material in the course it is well explained and the ideas about how to use the platform and the ideas of how to solve the problems are well-defined. The exercises are defined in such way that the student can immediately used what he/she learned from previous materials. It is a great introduction to Tensorflow.

por Alan F T

•28 de sep. de 2020

This is an excellent foundational course on a complex topic. I especially like the application of the course examples in real-world deployment type assignments. The use of colab is very nice, it is not a technology that I have used before however its 'jupyter' like interface is very comfortable and I like how it is deployed and accessible on virtual machines all the time. The only criticism I have is that the assignment grading is a little rigid and there is not a lot of valuable feedback if fails (i.e. it could be badly coded or it could be something as trivial as using the wrong number of nodes in a layer! - this could be explained better I feel)

por Jeremy V K

•24 de jun. de 2020

Amazing class! As an absolute beginner, I wanted to learn machine learning and AI but didn't even known where to start. I even started a different machine learning course which I could not follow well, and expected similar results with this class. But this class, this class is aimed at beginners, no need for any pre-requisite knowledge and the google colab environment ensures that it doesn't matter what hardware you currently have, since its all done in the cloud. Also the practice notebooks are very detailed about each sections of the program. I'm not a master at machine learning, but atleast after this course I feel that I too can learn it.

por Dhamotharan B

•29 de jun. de 2020

I have used Tensorflow in my projects but never know some of the tricks which could improve the model.I was very dependent on transfer learning. But, now after getting to know much about Tensorflow, I feel so confident . A basic understanding of how model works with Tensorflow is essential and Laurence Monorey provides you some of those basic tricks and concepts of how neural networks works with Tensorflow. I can assure anyone here taking this course would definitely give a try to adjust the way they have approached towards implementing Tensorflow in their projects. Thanks Andrew and Coursera for bring up this initiative.

- Analista de datos de Google
- Gestión de proyectos de Google
- Diseño de experiencia del usuario (UX) de Google
- Soporte de TI de Google
- Ciencia de datos de IBM
- Analista en datos de IBM
- Análisis de datos de IBM con Excel y R
- Analista de ciberseguridad de IBM
- Ingeniería de Datos de IBM
- Desarrollador de la nube de pila completa de IBM
- Marketing en redes sociales: Facebook
- Analítica del marketing de Facebook
- Representante de desarrollo de ventas de Salesforce
- Operaciones de venta de Salesforce
- Contabilidad en Intuit
- Prepárate para una certificación en Google Cloud: arquitecto de la nube
- Prepárate para una certificación en Google Cloud: ingeniero de datos de la nube
- Lanza tu carrera profesional
- Prepárate para una certificación
- Avanza en tu carrera

- cursos gratuitos
- Aprende un idioma
- python
- Java
- diseño web
- SQL
- Cursos gratis
- Microsoft Excel
- Administración de proyectos
- seguridad cibernética
- Recursos Humanos
- Cursos gratis en Ciencia de los Datos
- hablar inglés
- Redacción de contenidos
- Desarrollo web de pila completa
- Inteligencia artificial
- Programación C
- Aptitudes de comunicación
- Cadena de bloques
- Ver todos los cursos

- Habilidades para equipos de ciencia de datos
- Toma de decisiones basada en datos
- Habilidades de ingeniería de software
- Habilidades sociales para equipos de ingeniería
- Habilidades para administración
- Habilidades en marketing
- Habilidades para equipos de ventas
- Habilidades para gerentes de productos
- Habilidades para finanzas
- Cursos populares de Ciencia de los Datos en el Reino Unido
- Beliebte Technologiekurse in Deutschland
- Certificaciones populares en Seguridad Cibernética
- Certificaciones populares en TI
- Certificaciones populares en SQL
- Guía profesional de gerente de Marketing
- Guía profesional de gerente de proyectos
- Habilidades en programación Python
- Guía profesional de desarrollador web
- Habilidades como analista de datos
- Habilidades para diseñadores de experiencia del usuario

- MasterTrack® Certificates
- Certificados profesionales
- Certificados universitarios
- MBA y títulos de grado en negocios
- Títulos de grado en ciencias de los datos
- Títulos en ciencias informáticas
- Títulos de grado en Análisis de datos
- Títulos de grado en salud pública
- Títulos de grado en Ciencias Sociales
- Títulos de grado en administración
- Títulos de grado de las principales universidades europeas
- Maestrías
- Licenciaturas
- Títulos de grado con trayectoria de desempeño
- Cursos BSc
- ¿Qué es una licenciatura?
- ¿Cuánto tiempo dura una Maestría?
- ¿Vale la pena hacer una MBA en línea?
- Siete maneras de pagar la escuela de posgrado
- Ver todos los certificados