- classification tasks
- supervised learning
- discriminative deep learning models
- artificial neural networks (ANNs)
- deep neural networks (DNNs)
- convolutional neural networks (CNNs)
- recurrent neural network (RNNs)
The Deep Learning Bootcamp serves as an extension of the existing 42AI bootcamps: Python & Machine Learning, aiming to introduce participants to advanced concepts in deep learning. Emphasizing consistency with former bootcamps, this program focuses on introducing new notions and concepts specific to deep learning.
Two key considerations include maintaining alignment with past bootcamps and adjusting difficulty levels accordingly.
Given its nature as an extension, the bootcamp may not revisit fundamental concepts already covered in previous bootcamps. Instead, it will delve into more advanced topics across five modules. These modules will include hands-on practices on building neural networks “from scratch” using Python code, primarily focusing on Deep Neural Networks (DNNs) while also touching upon the basics of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
Each module is structured to span one day, although the complexity of the content may necessitate additional time for thorough understanding and application. Adjustments in the duration of each module will be made based on the difficulty of the material covered to ensure comprehensive learning experiences for participants.
This project is a Deep Learning bootcamp WORKING IN PROGRESS by 42 AI.
As notions seen during this bootcamp can be complex, we very strongly advise students to have previously done the following bootcamps:
42 Artificial Intelligence is a student organization of the Paris campus of the school 42. Our purpose is to foster discussion, learning, and interest in the field of artificial intelligence, by organizing various activities such as lectures and workshops.
Introduction to Deep Learning, covering fundamental concepts and principles.
Activation function(sigmoid), Cost functions
Vectorization, Model evaluation metrics
Gradient descent
Forward and backward propagation steps
Delve into Deep Neural Networks (DNNs), understanding their architecture, training process, and applications.
Deep neural network architecture(neurons/units/nodes, layers), Fully connected layers
Backpropagation, Training deep networks, Optimization algorithms(SGD)
Regularization techniques (L2 regularization)
Explore Convolutional Neural Networks (CNNs), focusing on image recognition and other computer vision tasks.
Convolutional layers, Pooling layers, Stride and padding
Image preprocessing, Transfer learning
Discover Recurrent Neural Networks (RNNs), emphasizing their sequential data processing capabilities for tasks such as time series analysis.
Recurrent neural network architecture
Sequence modeling, Time series forecasting
Advanced topics in Deep Learning, and optimization techniques
Advanced optimization techniques (momentum, RMSprop)
Learning rate scheduling, Batch normalization