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Anton
Build and train neural network models using TensorFlow 2.x

	
Build and train models for binary classification
	
Build and train models for multi-class categorization
	
Build sequential models with multiple layers
	
Build, compile and train machine learning (ML) models
	
Identify strategies to prevent overfitting, including augmentation and dropout
	
Plot loss and accuracy of a trained model
	
Preprocess data to get it ready for use in a model
	
Use callbacks to trigger the end of training cycles
	
Use datasets from different sources
	
Use datasets from tf.data.datasets
	
Use datasets in different formats, including json and csv
	
Use models to predict results
	
Use pretrained models (transfer learning)
	
Use TensorFlow 2.x
	
Ensure that inputs to a model are in the correct shape
	
Ensure that you can match test data to the input shape of a neural network
	
Ensure you can match output data of a neural network to specified input shape for test data
	
Extract features from pre-trained models
	
Understand batch loading of data


Canon – Session #1
Classification d’iris – #1
Classification d’iris – #2
Classification d’iris – #3
Classification d’iris – #4
DWQA Ask Question
DWQA Questions
Image classification

	
How to use convolutions
	
Process real-world image datasets
	
CNN with Conv2D and pooling
	
ImageDataGenerator
	
Understand how ImageDataGenerator labels images
	
Use real-world images
	
Image augmentation


Introduction
Introduction à l’Intelligence Artificielle

	
Définition de l’IA
	
L’IA – Introduction
	
L’IA nous concerne tous
	
Les réseaux de neurones
	
Quizz


Membres
Natural language processing (NLP)

	
Build natural language processing systems using TensorFlow
	
Add RNN and GRU
	
Binary categorization
	
LSTMs
	
Multi-class categorization
	
Prepare text to use in TensorFlow models
	
RNNS, LSTMs, GRUs and CNNs in models
	
Train LSTMs
	
Use word embeddings


Page d’exemple
RNN, LSTM pour Time Series
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Time series, sequences and predictions

	
Prepare data for time series learning
	
Adjust the learning rate dynamically in time series, sequence and prediction models
	
Identify and compensate for sequence bias
	
Prepare features and labels
	
Train, tune and use time series, sequence and prediction models
	
Understand MAE and how it can be used to evaluate accuracy of sequence models
	
Use RNNs and CNNs for time series, sequence and forecasting models
	
Use TensorFlow for forecasting
	
Identify when to use trailing versus centred windows