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TensorFlow for Deep Learning problems and usecases, best one!!!
What do you like best about the product?
There are so many points which I liked aboutTensorFlow i.e. It is fast and it's scalability on the larger dataset, Witht the help of TensorFlow I am able to write customisable evaluation functions.
What do you dislike about the product?
Overall performance of TensorFlow is good but the documentation of TensorFlow can be improved, Sometime i felt inconsistency in the algorithm which can be further optimised.
What problems is the product solving and how is that benefiting you?
With the help of TensorFlow I have implemented and solved multiple problems of Deep learning. I have implemented deep neural networks for solving the problems in the medical domain.
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Easy to use and has a lot of inbuilt functionality and support for algorithms
What do you like best about the product?
Tensorflow is an excellent library for implementing linear algebra equations and algorithms. It also has Keras as its inbuilt module, a perfect module for deep learning and implementing neural network models.
I use it majorly for implementing and training deep learning models. It provides high customizability for defining our loss functions, activation functions, etc.
I use it majorly for implementing and training deep learning models. It provides high customizability for defining our loss functions, activation functions, etc.
What do you dislike about the product?
The Keras interface provided inside TensorFlow is not the same as externally importing Keras. There are a few differences which can someone comfortable with Keras make several mistakes while developing using Tensorflow.
It also sometimes shows some errors which are easy to understand and often not even related to the code, but the running environment/kernel instead.
It also sometimes shows some errors which are easy to understand and often not even related to the code, but the running environment/kernel instead.
What problems is the product solving and how is that benefiting you?
Implementing deep neural networks is straightforward. Implementing non-linear neural networks also becomes more accessible through the functional interface provided by the module.
The ability to implement custom algorithms to be used with neural networks also helps when writing research papers or implementing one.
The ability to implement custom algorithms to be used with neural networks also helps when writing research papers or implementing one.
Recommendations to others considering the product:
If you are willing to get started with deep learning and deep neural networks, Tensorflow is one of the best options due to its applications, usability, and ease of use.
Tensorflow review
What do you like best about the product?
Tensorflow is very mature deep learning library which is heavily used in production scenarios. I particularly like the tensorflow-lite version which comes along which reduces the size of the model and is good to deploy in edge devices.
What do you dislike about the product?
It requires a little more coding as compared to pytorch. Pytorch is more pythonic and hence is easier to learn and implement
What problems is the product solving and how is that benefiting you?
For deep learning model development for the industry I am working in
Like why would you use another ML platform
What do you like best about the product?
Python based and API is intuitive. Keras is great and uses the Tensorflow library. I used scikit-learn prior and it was so much harder to understand and require way more code to get the same things done. The user-friendly interface is honestly the best part of Tensorflow/Keras.
What do you dislike about the product?
Not a lot, but for Tensorflow Lite, a user manual to port to other boards would be great. I wanted to use Tensorflow Lite on my TM4C123GXL board, but it's not a supported platform. I am sure there is a way to get it running on any board, I just do not know how.
What problems is the product solving and how is that benefiting you?
Mainly educational purposes. I wanted to create a fire detection program that I hoped could be used to combat wildfires. I haven't really gotten the time to do this, but I still want to do it.
Recommendations to others considering the product:
Look no further, this is the ML platform to use.
Tensorflow ML platform
What do you like best about the product?
Very powerful platform with Keras and other ml/dl libraries
TFRecord is very efficient way of handling/storing data
TFRecord is very efficient way of handling/storing data
What do you dislike about the product?
Very heavy software for inferencing though TFLite is good for mobile
What problems is the product solving and how is that benefiting you?
Training ML models for computer vision, natural language processing and graph neural networks.
Most mathematically-oriented ML framework
What do you like best about the product?
For people who grew up learning the math of backprop, who enjoy thinking about syntax trees and computation graphs, Tensorflow will allow you to make full use of you that insight. Interesting loss functions like Wasserstein loss (where the gradient itself enters as part of the loss function) enter naturally.
What do you dislike about the product?
The mix between Tensorflow v1 and v2 code is somewhat difficult to learn, if you only get into it now. Tensorflow v2 is modeled much more on Keras, and is designed for you to particular architectures and pipelines. That's great, but if you then want to mix that with the flexibility of v1, you run into a lot of pain.
What problems is the product solving and how is that benefiting you?
I've used Tensorflow as a black-box optimizer for searching for Quantum Error-Correcting Codes. That probably doesn't sound like Machine Learning, right? But it's gradient descent on large parallel datasets, so hey, it works. I've also seen it for e.g. physics simulations, card game simulation, a wide variety of "parallel" tasks. In the most liberal interpretation, Tensorflow is "CUDA but better": a way to use your GPU for parallel tasks in a general setting.
Graphical computation in deep learning
What do you like best about the product?
The fact is that you can create the network and then do computation all at once. The computation is well optimized to run on GPU. The tensorboard support enables us to view the metric like accuracy and weights during the training which is absent in other deep learning packages
What do you dislike about the product?
The high level api is not present in the package itself. For that we need to use keras or other packages which is build on top of this but these high level API is not native to tensorflow
What problems is the product solving and how is that benefiting you?
Building and training deep neural networks. Getting deep into the training process and visualizing it does makes a lot of difference in getting very accurate models.
TensorFlow for AI Model Development
What do you like best about the product?
The most valuable part of TensorFlow is the Tensorboard. While training the AI model development, it provides better visualization for debugging and error handling.
What do you dislike about the product?
The least liked part of TensorFlow is it's implementation speed. In comparison to another deep learning framework, development time is higher in TensorFlow
What problems is the product solving and how is that benefiting you?
I am using TensorFlow Framework for complex Neural Network Implementations and Face Recognition deep learning model development.
Best Platform for Building Deep learning models and Train them
What do you like best about the product?
It was easy to get on with Tensorflow compared to other machine learning libraries. There are tons of community support, tutorials, videos, and even pre-build models to learn and get maximum out of it in a short time. And what's more, it's completely free and open-source.
What do you dislike about the product?
There is nothing to say in this section. Tensorflow has it all, and I love it. I haven't faced a serious issue yet, and even If I did, the community is there to solve them happily.
What problems is the product solving and how is that benefiting you?
I used Tensorflow for part of my final year research project. It was fun to learn and train the model as for my requirement. In the end, I was able to implement it without issues, and it was a success. I realized the true potential of Tensorflow.
Recommendations to others considering the product:
It's a must-use library for anyone who is into deep learning and model creation and training.
Framework with ease access of AI ML APIs
What do you like best about the product?
Graph visualisation in tensorflow is much better.
Frequent updates as its backed by Google.
Frequent updates as its backed by Google.
What do you dislike about the product?
Tensorflow lacks behind in terms of computation and speed.
It's only supported in NVIDIA GPUs.
It's only supported in NVIDIA GPUs.
What problems is the product solving and how is that benefiting you?
Tensorflow helps us to train and deploy deep learning based model with ease.
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