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Jean de Nyandwi Profile picture
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25 Sep, 9 tweets, 3 min read
Here are 7 samples from what's coming tomorrow:

1. Data visualization with Seaborn

◆Relational Plots
◆Distribution Plots
◆Categorical Plots
◆Regression Plots
◆Multiplots
◆Matrix Plots: Heat and Cluster Maps
◆Style and Color

colab.research.google.com/drive/1Qkf53B4…
2. Exploratory Data Analysis

◆A quick look into the dataset
◆Summary statistics
◆Finding the basic information about the dataset
◆Checking missing data
◆Checking feature correlations

colab.research.google.com/drive/1iMpQOWH…
3. A Friendly Intro to Machine Learning

◆Intro to ML Paradigm
◆Machine Learning Workflow
◆Evaluation Metrics
◆Handling Underfitting and Overfitting

colab.research.google.com/drive/14uySoOh…
4. Ensemble methods

◆Voting methods
◆Bagging methods
◆Gradient Boost
◆AdaBoost
◆Stacking methods
◆XGBoost

colab.research.google.com/drive/1u57rVSW…
5. Neural Networks for Basic Classification Tasks

◆Intro to Classification with TensorFlow
◆Binary Classification
◆Going Beyond Binary Classifier to Multiclassifier

colab.research.google.com/drive/16XMM--4…
6. Computer Vision and Convolutional Neural Networks(CNNs)

◆Intro to Convolutional Neural Networks?
◆A Typical Architecture of Convolutional Neural Networks
◆Coding ConvNets: Cifar10 Classifier

colab.research.google.com/drive/1lXwPKrg…
7. Recurrent Neural Networks(RNNs)

◆Intro to Recurrent Neural Networks
◆RNNs In Practice: Movies Sentiment Analysis
◆LSTMs for News Classification
◆Using Stacked LSTMs
◆Gated Recurrent Unit(GRU)

colab.research.google.com/drive/1xqXQURK…
These were samples.

More other 27+ notebooks will be released tomorrow.

They will be accessible on Github, @DeepnoteHQ, and other platforms that make it easy to view and play with notebooks.

You can follow @Jeande_d to know when this is coming and more future content.
For any feedback, error, or any suggestion about these samples,

You're welcome to share it here.

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More from @Jeande_d

Jean de Nyandwi Profile picture
26 Sep
Releasing a complete machine learning package containing over 30 end to end notebooks for:

◆Data analysis
◆Data visualization
◆Data cleaning
◆Classical ML
◆Computer vision
◆Natural language processing

Everything is now accessible here:

github.com/Nyandwi/machin…
Every single notebook is very interactive.

It starts with a high-level overview of the model/technique being covered and then continues with the implementation.

And wherever possible, there are visuals to support the concepts.
Here is an outline of what you will find there:

PART 1 - Intro to Programming and Working with Data

◆Intro to Python for Machine Learning
◆Data Computation With NumPy
◆Data Manipulation with Pandas
◆Data Visualization
◆Real EDA and Data Preparation Image
Read 10 tweets
Jean de Nyandwi Profile picture
20 Sep
TensorFlow or PyTorch?

Forget about numbers. They are both great at what they do, which is putting machine learning codes together.
TensorFlow is most popular in industries, and PyTorch in research organizations/academics,

but the number of industries that use PyTorch and the number of researches made with TensorFlow have been all increasing.
If you are choosing what to learn for the first time, what is the best than the other does not really matter that much.

Focus on one, know its ins and outs, avoid going back and forth learning all of them, and let everybody else use their favorite tools.
Read 5 tweets
Jean de Nyandwi Profile picture
20 Sep
The key differences between shallow learning and deep learning models:

Shallow learning models:

◆ Most of them are simple and require less hyper-parametrization
◆ They need the features to be pre-extracted
◆ They are best suited for tabular datasets
◆ Their architectural changes are very limited.
◆ They don't require huge computation resources
◆ Their results are interpretable than deep learning models
◆ Because of the limit in their design change, there are little researches going on in these models.
Example of shallow learning models:

◆Linear and logistic regression
◆Support vector machines
◆Decision trees
◆Random forests
◆K-Nearest neighbors
Read 9 tweets
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
10 Sep
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.
Read 4 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

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