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Questions and Answers
What book is recommended for Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville?
Deep Learning
Which course at collège de France is taught by Yann Lecun?
Yann Lecun's course
What is the main focus of the course on Neural Networks?
Understanding neurons, their connections, training processes, and the deep learning revolution.
What is deep learning often referred to as?
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The human brain consists of formal neurons and weighted connections similar to artificial neural networks.
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The output activation of a formal neuron is obtained by applying an activation function _____ the weighted sum of inputs.
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Match the following layer types with their descriptions:
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What is the learning scheme for Neural Networks described in the content?
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Which of the following is a principle of Gradient Descent for tuning weights in Neural Networks?
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Deep learning models typically have many layers and millions of parameters.
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What is one of the challenges in training deep neural networks mentioned in the content?
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____________ is a famous dataset with over 14 million images and 1000+ classes used in deep learning.
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Study Notes
Introduction to Neural Networks
- In 2010, machine learning began to evolve with the emergence of "Deep Learning".
- Before 2010, machine learning was limited to very specific tasks and was not impressive.
Human Learning vs Machine Learning
- Human learning is complex and involves combining many simple entities (neurons) to model complex functions.
- Machine learning is a simplified mathematical model of the human brain, where neurons are connected through synapses.
- Learning in machine learning involves optimizing the connections between neurons.
Formal Neuron
- A formal neuron is a mathematical representation of a biological neuron.
- It consists of a weighted sum of inputs, an activation function, and an output.
- The activation function can be linear or non-linear (e.g. ReLU, tanh, sigmoid).
Topologies
- Neurons can be arranged in different topologies, including feedforward, recurrent, and random connections.
- In a feedforward network, neurons are arranged in layers, and information flows from input to output.
- Convolutional neural networks (CNNs) are a type of feedforward network that use convolutional and pooling layers to extract features.
Training Neural Networks
- The goal of training a neural network is to find the optimal parameters (weights and biases) that minimize a loss function.
- Gradient descent is an optimization algorithm used to update the parameters in the direction of the negative gradient of the loss function.
- Backpropagation is a method for computing the gradients of the loss function with respect to the parameters.
Deep Learning
- Deep learning involves using many layers (often >3) of neurons to model complex functions.
- Deep neural networks can be trained using large datasets and require significant computational resources.
- Vanishing gradient is a problem in deep learning, where the gradients used to update the parameters become smaller as they propagate through the network.
Recent Success of Deep Learning
- Deep learning has achieved state-of-the-art performance in various computer vision tasks, including image classification, object detection, and semantic segmentation.
- Transfer learning is a technique used in deep learning, where a pre-trained network is fine-tuned on a smaller dataset to adapt to a new task.### Deep Learning Challenges
- High-dimensional data and structured output
- Limited pixel-labeled examples
- Challenges addressed by neural networks since 2015
Fully Convolutional Networks (FCN)
- Lightweight and require few examples
- Easy to train
Semantic Segmentation
- SegNet (demo at http://mi.eng.cam.ac.uk/projects/segnet/)
Image Captioning
- CNN/LSTM generative architecture
- CNN layer extracts features (pre-trained on ImageNet)
- LSTM layers generate output signal (pre-trained)
- Backpropagation on COCO dataset for linking both architectures
- Results from Google system (Xu, 2015)
Transformer Architecture
- Introduced in "Attention Is All You Need" (Vaswani, et al., 2017)
- Main architecture for any sequence problem
- Encoder and decoder are transformer layers
- Based on multi-head attention and linear layers
- Uses positional encoding
Applications of Transformers
- Encoder can be replaced by another encoder (DAN)
- Encoder alone can be used (BERT)
- Decoder alone can be used (GPT)
Deep Learning in Practice
- Many neural networks libraries available
- TensorFlow (Google, Python)
- Keras (Google, Python)
- PyTorch (Facebook, Python)
- Example code for VGG16 in Keras
Bibliography
- Interesting links: Yann Lecun's course at College de France, H. Larochelle's YouTube channel, and "Deep Learning" book by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
- Relevant courses: Machine Learning (ITI4.1), Deep Learning (ITI4.2), Advanced Machine Learning (ITI5.1), and Advanced Deep Learning (ITI5.2)
- Listed research papers and books on neural networks and deep learning
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Description
This quiz covers the basics of neural networks, including human vs machine learning, formal neurons, and topologies, as well as deep learning principles and successes.
