MLP Mixer Is All You Need?

Understanding MLP-Mixers from beginning to the end, with TF Keras code

Earlier this May, a group of researchers from Google released a paper “MLP-Mixer: An all-MLP Architecture for Vision” introducing their MLP-Mixer ( Mixer, for short ) model for solving computer vision problems. The research suggests that MLP-Mixer attains competitive scores on image classification benchmarks such as the ImageNet.

One thing that would catch every ML developer’s eyes, is that they haven’t used convolutions in their architecture. Convolutions have reigned computer vision since long as they are efficient in extracting spatial information from images and videos. Recently, Transformers, that were originally used for NLP problems, have shown remarkable results in computer vision problems as well. The research paper for MLP-Mixer suggests,

In this paper we show that while convolutions and attention are both sufficient for good performance, neither of them are necessary.

I have used MLP-Mixers for text classification as well,

We’ll discuss more on MLP-Mixer’s architecture and underlying techniques involved. Finally, we provide a code implementation for MLP-Mixer using TensorFlow Keras.

📃 Contents

  1. 👉 Dominance of Convolutions, advent of Transformers
  2. 👉 Multilayer Perceptron ( MLP ) and the GELU activation function
  3. 👉 MLP-Mixer Architecture Components
  4. 👉 The End Game
  5. 👉 More projects/blogs/resources from the author

#machine-learning #artificial-intelligence #neural-networks #python #tensorflow #mlp mixer is all you need?

What is GEEK

Buddha Community

MLP Mixer Is All You Need?

MLP Mixer Is All You Need?

Understanding MLP-Mixers from beginning to the end, with TF Keras code

Earlier this May, a group of researchers from Google released a paper “MLP-Mixer: An all-MLP Architecture for Vision” introducing their MLP-Mixer ( Mixer, for short ) model for solving computer vision problems. The research suggests that MLP-Mixer attains competitive scores on image classification benchmarks such as the ImageNet.

One thing that would catch every ML developer’s eyes, is that they haven’t used convolutions in their architecture. Convolutions have reigned computer vision since long as they are efficient in extracting spatial information from images and videos. Recently, Transformers, that were originally used for NLP problems, have shown remarkable results in computer vision problems as well. The research paper for MLP-Mixer suggests,

In this paper we show that while convolutions and attention are both sufficient for good performance, neither of them are necessary.

I have used MLP-Mixers for text classification as well,

We’ll discuss more on MLP-Mixer’s architecture and underlying techniques involved. Finally, we provide a code implementation for MLP-Mixer using TensorFlow Keras.

📃 Contents

  1. 👉 Dominance of Convolutions, advent of Transformers
  2. 👉 Multilayer Perceptron ( MLP ) and the GELU activation function
  3. 👉 MLP-Mixer Architecture Components
  4. 👉 The End Game
  5. 👉 More projects/blogs/resources from the author

#machine-learning #artificial-intelligence #neural-networks #python #tensorflow #mlp mixer is all you need?

Ian  Robinson

Ian Robinson

1623938520

Do I need Big Data? And if so, how much?

Many companies follow the hype of big data without understanding the implications of the technology.

I call myself a “Big Data Expert”. I have tamed many animals in the ever growing Hadoop zoo like HBase, Hive, Oozie, Spark, Kafka, etc… I helped companies to build and structure their Data Lake using appropriate subsets of these technologies. I like to wrangle with data from multiple sources to generate new insights (or to confirm old insights with evidence). I love to build Machine Learning models for predictive applications. So, yes, I would say that I am well experienced with many facets of what people would call “Big Data”.

But at the same time, I became more and more skeptical of blindingly following the promises and the hype without understanding all the consequences and without evaluating the alternatives.

#hadoop #big-data #nosql #do i need big data? and if so, how much? #need big data

David mr

David mr

1624305600

SAFEMOON UPDATE - ALL YOU NEED TO KNOW ABOUT SAFEMOON AND SAFEMOON PREDICTION

SAFEMOON UPDATE - ALL YOU NEED TO KNOW ABOUT SAFEMOON AND SAFEMOON PREDICTION

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Steps You Should Follow TO Successfully Train MLP

Traning Multi-layer perceptron is not an easy task and there are many steps that you must follow to train an MLP and to get most out of it and if you just miss any of these steps then everything just goes into the dust. So please don’t forget to do any of these steps before creating your MLP model.

Steps Required

  1. Data Preprocessing
  2. Weights Initialization
  3. Choosing the right activation function
  4. Batch Normalization
  5. Adding Dropouts
  6. Using Optimizer
  7. Hyperparameters
  8. Loss-Function

1. Data Preprocessing

Data preprocessing is one of the most important steps in any machine learning or deep learning projects and if you not going to use data preprocessing then any model that you make is simply useless.

Data that we get from the real world is incomplete, inconsistent, inaccurate (contains errors or outliers), and often lacks specific attribute values/trends. So this is where the** Data Preprocessing** comes for the rescue . In data preprocessing we basically clean the data, fill up the missing value find and remove then outlier.

Steps of Data Preprocessing:

1 **Data Cleaning: **The data we get may have many irrelevant and missing data points so to handle this part, data cleaning is done. It involves handling of missing data, noisy data, etc. and there are many more steps involved in this.

2 **Feature Scaling: **It is done in order to bring the data from a different scale to the same scale by bringing all the data values in a specified range (-1.0 to 1.0 or 0.0 to 1.0). We do Data Standardization to rescales data to have a mean of 0 and a standard deviation of 1 (unit variance). We use **_Normalization _**to rescales the values into a range of [0,1]

2. Weights Initialization

Weight initialization is used to prevent activation layers outputs from exploding gradient or vanishing gradients problem during the course of a forward and backward propagation through a deep neural network.

Weight initialization is mostly dependent on the activation function that you are using. If you are having Sigoid or tanh as your activation function then it’s better to use Xavier Initialization or Glorot normal initializer and withthe activation function like ReLu it is better to use He Normal

#deep-learning #mlp #machine-learning #deep learning

Is MLP Better Than CNN & Transformers For Computer Vision?

Earlier this month, Google researchers released a new algorithm called MLP-Mixer, an architecture based exclusively on multi-layered perceptrons (MLPs) for computer vision. The MLP-Mixer code is now available on GitHub.

Read more: https://analyticsindiamag.com/is-mlp-better-than-cnn-transformers-for-computer-vision/

#mlp #cnn #computervision