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+# What is Deep Learning?
+
+Deep Learning is an umbrella term for an approach to Machine Learning
+that makes use of "deep" Neural Networks, a kind of models originally
+inspired by the function of biological brains.  These days, Deep
+Learning is probably the most visible area of Machine Learning, and it
+has seen amazing successes in areas like Computer Vision, Natural
+Language Processing and, in combination with Reinforcement Learning,
+more complicated settings such as game playing, decision making and
+simulation.
+
+A crucial element of the success of Deep Learning ("DL" in what
+follows) has been the existence of software frameworks and runtimes
+that facilitate the creation of Neural Network models and their
+execution for inference.  Examples of such frameworks include
+Tensorflow, (Py)Torch and onnx.  ML.NET provides access to some of
+these frameworks, while maintaining the familiar pipeline interface.
+In this way, users of ML.NET can take advantage of some
+state-of-the-art models and applications of DL at a lower cost than
+the steep learning curve learning that other DL frameworks require.
+
+# Deep Learning vs Machine Learning?
+
+As mentioned above, DL relies on "Neural Network" models, in contrast
+with "traditional" Machine Learning techniques (which use a wider
+variety of architectures, such as, for example, generalized linear
+models, decision trees or Support Vector Machines).  The most
+immediate, practical implication of this difference is that DL methods
+may be better or worse suited for some kind of data.  The performance
+of DL methods on images, on textual and on other non- or
+less-structured data has been well documented in the literature.
+Traditional Machine Learning methods such as gradient-boosted trees
+(XGBoost, LightGBM and CatBoost) seem to still have an edge when it
+comes to tabular data.  The best approach is always to experiment with
+your particular data source and use case and determine for yourself,
+and ML.NET makes this experimentation relatively straightforward and
+pain-free.
+
+# Neural Network architectures
+
+A crucial differentiating characteristic of DL from other classes (or
+schools) of ML is the use of artificial Neural Networks as models.  At
+a high-level, one can think of a Neural Network as a configuration of
+"processing units" where the output of each unit constitutes the input
+of another.  Each of these units can take one or many inputs, and
+essentially carries out a weighted sum of its inputs, applies an
+offset (or "bias") and then a non-linear transformation function
+(called "activation").  Different arrangements of these relatively
+simple components have been proven surprisingly rich to describe
+decision boundaries in classification, regression functions and other
+structures central to ML tasks.
+
+The past decade has seen an explosion of use cases, applications and
+techniques of DL, each more impressive than the last, pushing the
+boundaries of what functionalities we thought a computer program could
+feature.  This expansion is fueled by an increasing variety of
+operations that can be incorporated into Neural Networks, by a richer
+set of arrangments that these operations can be configured in and by
+improved computational support for these improvements.  In general, we
+can categorize these new Neural Architectures, and their use cases
+they enable, in (a more complete description can be found [here](https://learn.microsoft.com/en-us/azure/machine-learning/concept-deep-learning-vs-machine-learning#artificial-neural-networks) ):
+
+* Feed-forward Neural Network
+* Convolutional Neural Network
+* Recurrent Neural Network
+* Generative Adversarial Network
+* Transformers
+
+# What can I use deep learning for?
+
+As stated above, the scope of application of DL techniques is rapidly
+expanding.  DL architectures, however, have shown amazing
+(close-to-human in some cases) performance in tasks having to do with
+"unstructured data": images, audio, free-form text and the like.  In
+this way, DL is constantly featured in image/audio classification and
+generation applications.  When it comes to text processing, more
+generally Natural Language Processing, DL methods have shown amazing
+results in tasks like translation, classification, generation and
+similar.  Some of the more spectacular, recent applications of ML,
+such as "[Stable Diffusion](https://en.wikipedia.org/wiki/Stable_Diffusion)" are powered by sophisticated, large Neural
+Network architectures.
+
+# Deep learning in ML.NET
+
+A central concern of DL is what Neural Network architecture (specific configuration of operations) will the model have, and to this end, DL frameworks like Tensorflow and Pytorch feature expressive Domain-Specific Languages to describe in detail such architectures.  ML.NET departs from this practice and concentrates on the consumption of pre-trained models (i.e., architectures that have been specified *and* trained in other frameworks).
+
+# Train custom models
+
+# Image classification
+
+# Text classification (Needs tutorial)
+# Sentence Similarity (Needs tutorial - P1)
+
+# Consume pretrained models
+
+# TensorFlow  https://learn.microsoft.com/en-us/dotnet/machine-learning/tutorials/text-classification-tf
+# ONNX https://github.com/dotnet/csharp-notebooks/blob/main/machine-learning/E2E-Text-Classification-API-with-Yelp-Dataset.ipynb