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Jean de Nyandwi Profile picture
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10 Sep, 4 tweets, 2 min read
Neural networks are hard to train. The more they go deeper, the more they are likely to suffer from unstable gradients.

Gradients can either explode or vanish, and either of those can cause the network to give poor results.

A short thread on the neuralnets training issues
The vanishing gradients problem results in the network taking too long to train(learning will be very slow), and the exploding gradients cause the gradients to be very large.
Although those problems are nearly inevitable, the choice of activation function can reduce their effects.

Using ReLU activation in the first layers can help avoid vanishing gradients.

Careful weight initialization can also help, but ReLU is by far the good fix.
I have found these two resources to explain well the unstable gradients issue in neural network training:

Why are deep neural networks hard to train? by @michael_nielsen

neuralnetworksanddeeplearning.com/chap5.html#the…

Discussion: Why is it hard to train deep neural networks?

stats.stackexchange.com/questions/2627…

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More from @Jeande_d

Jean de Nyandwi Profile picture
11 Sep
Popular deep learning architectures:

◆ Densely connected neural networks
◆ Convolutional neural networks
◆ Recurrent neural networks
◆ Transformers

Let's talk about these architectures and their suites of datasets in-depth 🧵
Machine learning is an experimentation science. An algorithm that was invented to process images can turn out to work well on texts too.

The next tweets are about the main neural network architectures and their suites of datasets.
1. Densely connected neural networks

Densely connected networks are made of stacks of layers that go from the input to the output.

Generally, networks are organized into layers. Each carry takes input data, processes it, and gives the output to the next layer.
Read 34 tweets
Jean de Nyandwi Profile picture
6 Sep
Machine learning is the science of teaching the computer to do certain tasks, where instead of hardcoding it, we give it the data that contains what we want to achieve, and its job is to learn from such data to find the patterns that map what we want to achieve and provided data.
These patterns or (learned) rules can be used to make predictions on unseen data.
A machine learning model is nothing other than a mathematical function whose coefficient and intercept hold the best (or learned) values representing the provided data & what we want to achieve.

In ML terms, coefficients are weights, intercepts are biases.
Read 20 tweets
Jean de Nyandwi Profile picture
31 Aug
Everyone talks about big data but getting good data in a big amount is not always easy.

You can do much with small data as long as it is good.

A thread 🧵: Getting the most results with small data
The two notable techniques that can give huge results when working with small data are

◆ Data augmentation
◆ Transfer learning

Let's talk about them. We will use them in the context of images kind datasets but they can also be applied to other datasets such as texts.
1. Data augmentation

Data augmentation is the art of creating artificial (but realistic) data.

Not only does data augmentation expand the dataset,

but it also introduces some diversity in the training set (the reason why data augmentation is a cure for overfitting)
Read 12 tweets
Jean de Nyandwi Profile picture
27 Aug
Getting started with machine learning can be hard.

We are fortunate to have many & freely available learning resources, but most of them won't help because they skip the fundamentals or start with moonshots.

This is a thread on learning machine learning & structured resources.
1. Get excited first

The first step to learning a hard topic is to get excited.

Machine learning is a demanding field and it will take time to start understanding concepts & connecting things.
If you find it hard to understand what ML really is,

@lmoroney I/O 19 talk will get you excited. He introduces what machine learning really is from a programming perspective.



This talk never gets old to me.
Read 29 tweets
Jean de Nyandwi Profile picture
5 Aug
For many problems, a batch size of 32 works so well.

A batch size mostly affects training time. The larger the batch size, the faster the training.

The smaller, the slower training.
The only issue with the large batch size is that it requires many steps per epoch to reach optimal performance.

And you need to have a large dataset in order to have enough steps per epoch.

With that said, 32 is a good default value to try at first.
Here are 2 great papers that you can use to learn more:

Practical Recommendations for Gradient-Based Training of Deep Architectures: arxiv.org/pdf/1206.5533.…
Read 4 tweets
Jean de Nyandwi Profile picture
4 Aug
One of the techniques that have accelerated machine learning on insufficient real-world datasets is data augmentation.

Data augmentation is the art of creating artificial data.

For example, you can take an image, flip it, change color, and now you have a new image. Image
Yes, data augmentation is the art of creating artificial data to expand a given small dataset.

It has shown that it works so well(most of the time), and it remarkably handles overfitting.
Nearly most types of data can be augmented, but I have noticed that it works well in unstructured data(images, video, sound).

So, this thread will focus more on images, sounds, and videos.

For more about structured vs unstructured data 👇

Read 18 tweets

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