Share this page!

Jean de Nyandwi Profile picture
Twitter logo
24 Oct, 8 tweets, 4 min read
My summary of the week on Twitter ML:

◆ 3 threads on explaining complex concepts
◆ 2 on practical learning resources and
◆ 1 good news

🧵🧵
EXPLAINED CONCEPTS/IDEAS

#1 @fchollet on the nature of generalization in deep learning, clearly explaining interpolation and manifold hypothesis.

A long thread that is worth reading

#2 @svpino on what you didn't know about machine learning pipelines.

#3 @AlejandroPiad on the difference between P and NP, one of the most important theories in computer science.

PRACTICAL LEARNING RESOURCES

#1 A collection of Kaggle solutions notebooks in both deep learning, and classical machine learning.

From @rasbt

#2 A collection of hands-on projects in various fields: Computer vision, NLP, Reinforcement learning, data engineering, data visualization, and model deployment

From @TivadarDanka

WRAPPING UP THE WEEK ON SOME GOOD NEWS

%matplotlib inline is now optional when you import matplotlib.pyplot in a Jupyter notebook

From @aureliengeron

Thanks for reading!

Follow @Jeande_d for more machine learning content.

• • •

Missing some Tweet in this thread? You can try to force a refresh
　

Keep Current with Jean de Nyandwi

Jean de Nyandwi Profile picture

Stay in touch and get notified when new unrolls are available from this author!

Read all threads

This Thread may be Removed Anytime!

PDF

Twitter may remove this content at anytime! Save it as PDF for later use!

Try unrolling a thread yourself!

how to unroll video
  1. Follow @ThreadReaderApp to mention us!

  2. From a Twitter thread mention us with a keyword "unroll"
@threadreaderapp unroll

Practice here first or read more on our help page!

More from @Jeande_d

Jean de Nyandwi Profile picture
26 Oct
The following are 5 main types of machine learning systems based on the level of supervision involved in the training process:

◆Supervised learning
◆Unsupervised learning
◆Semi-supervised learning
◆Self-supervised learning
◆Reinforcement learning

Let's talk about them...🧵
1. Supervised learning

This is the common most type of machine learning. Most ML problems that we encounter falls into this category.

As the name implies, a supervised learning algorithm is trained with input data along with some form of guidance that we can call labels.
In other words, a supervised learning algorithm maps the input data (or X in many textbooks) to output labels (y).

Labels are also known as targets and they acts as a description of the input data.
Read 30 tweets
Jean de Nyandwi Profile picture
18 Oct
Kaggle's 2021 State of Data Science and Machine Learning survey was released a few days ago.

If you didn't see it, here are some important takeaways 🧵
Top 5 IDEs

1. Jupyter Notebook
2. Visual Studio Code
3. JupyterLab
4. PyCharm
5. RStudio
ML Algorithms Usage: Top 10

1. Linear/logistic regression
2. Decision trees/random forests
3. Gradient boosting machines(Xgboost, LightGBM)
5. Convnets
6. Bayesian approaches
7. Dense neural networks(MLPs)
8. Recurrent neural networks(RNNs)
9. Transformers(BERT, GPT-3)
10. GANs
Read 12 tweets
Jean de Nyandwi Profile picture
17 Oct
Source of errors in building traditional programs:

◆Wrong syntaxes
◆Inefficient codes
Source of errors in machine learning:

◆Solving a wrong problem
◆Using a wrong evaluation metric
◆Not being aware of a skewed data
◆Inconsistent data preprocessing functions
More sources of errors in ML:

◆Putting too much emphasis on models than data
◆Data leakage
◆Training on the test data
◆Model and data drifts
Read 7 tweets
Jean de Nyandwi Profile picture
11 Oct
Python image processing libraries

◆Scikit-Image
◆Pillow
◆NumPy(image is just an array of pixels)

The following are more than image processing, they provide state-of-the-art computer vision and machine learning algorithms:

◆OpenCV
◆OpenMMLab
Also, most machine learning frameworks have image processing functions.

TensorFlow has tf.image and Keras took that further to image processing layers that you can insert inside the model.
tf.image: tensorflow.org/api_docs/pytho…
Read 4 tweets
Jean de Nyandwi Profile picture
8 Oct
The machine learning research community is very and very vibrant.

Here is what I mean...🧵🧵
In 1958, Frank Rosenblatt invented a perceptron, a very simple algorithm that would later turn out to be the core and origin of to days intelligent machines.
In essence, the perceptron is a simple binary classifier that can determine whether or not a given input belongs to a specific class.

Here is the algorithm of perceptron:
Read 27 tweets
Jean de Nyandwi Profile picture
7 Oct
The most useful courses are free. They are only challenging and hard to complete, which is why they are useful.

Here are 4 examples of the free machine learning courses that with enough dedication can help you get useful skills.

🧵
1. Machine Learning by Andrew Ng. on Coursera

Price: Free
Students: Over 4 million people

coursera.org/learn/machine-…
2. Full Stack Deep Learning by UC Berkeley

Price: Free

fullstackdeeplearning.com/spring2021/
Read 7 tweets

Did Thread Reader help you today?

Support us! We are indie developers!

This site is made by just two indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3/month or $30/year) and get exclusive features!

Become Premium

Too expensive? Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal Become our Patreon

Thank you for your support!

Follow Us on Twitter!

Like this author's thread?

Get emails when @Jeande_d has new unrolls!

Check out other threads from @Jeande_d!

Read all threads by @Jeande_d

:(