Benniah Salami , MSc's repositories
data-engineer-roadmap
Roadmap to becoming a data engineer in 2021
NLP-Tokenization-LargeDataSet
In this Notebook, we apply the same Tokenization and Sequencing principles to a Large Corpus of Text
TimeSeries-CNN
1D Convolutional layer Vs Full Convolutional layer (Best Results) ---Adjusted learning rates & Dilation Rates
TRANSFER_LEARNING_FLOWERS
Classification of Flowers using the MobileNET model through the transfer learning technique
Airflow_setup
Installing Airflow ---> Manual Vs Docker
Flask_API_Snowflake
A simple API built with Flask for fetching data from Snowflake ✲
Space-Exploration-NLP
A lab containing various exercises, concepts and advanced NLP with Spacy
CNN_FashionMNIST
A convolutional Neural Network using the Fashion MNIST dataset
Dog_Vs_Cat
Image classification of Dogs and Cats using CNNs and Augmentation
Flower_Classification
Flower Classification using CNNs
merge_requirements
merged-requirements
NLP-ComapringModels
Using LSTMS , CNNs , GRUs for a larger dataset
NLP-Padding-Truncating
Preparing Text , Applying Padding and Truncation to obtain sequences of equal length
NLP-SUBWORDS
In this Notebook , we break down our text into subwords and check how it impacts our Model
NLP-Tokenization
A quick introduction to the Tokenization of Text and Sequencing (Ordering Text) , How to deal with OOV( Out of Vocabulary Text )
NLP-TweakingYourModel
In this Notebook , we tweak certain variables of our initial model such as the Vocabulary size , embedding dimension and Maximum length to yield Better results
NLP-WordEmbeddings-Sentiment
We used a basic neural network together with word embeddings to predict Sentiment
PDF_TEXT_EXTRACTION
Extracting text from PDF using python and converting them into keywords for further analysis
PySpark
Some Sample Apache Spark Code for Data Engineering and Analytics
Saving_Loading_Downloading-MODELS
Various ways of Saving , Loading and Downloading machine Learning Models
Starwars_API_Request
Making requests to SWAPI using Pagination
TimeSeries-Introduction
Common patterns in Time Series data : Trends , Series , Seasonality ,Noise
TimeSeries-LSTM
Forecasting with an LSTM
TimeSeries-MovingAverage
Computes the mean of the past values within a particular time window
TimeSeries-NaiveForecasting
Splitting into Training and Validation
TimeSeries-RecurrentNeuarlNetworks
Simple RNNS , Sequence to Sequence and Sequence to Vector
TimeSeries-SimpleMachineLearning
Applying some simple machine learning in forecasting. Made use of learning rate and early stopping techniques to yield better Mean absolute error values. Also made use of dense layers.
TimeSeries-Stateful_RNNs
Forecasting using stateful RNNs.
TimeSeries-TimeWindows
Creating Time windows , Converting data set to Tensors , Creating inputs/Targets , "SuperFunction containing all steps"