EITC/AI/DLPP Deep Learning with Python and PyTorch
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EITC/AI/DLPP Deep Learning with Python and PyTorch is the European IT Certification programme on the fundamentals of programming deep learning in Python with PyTorch machine learning library.
The curriculum of the EITC/AI/DLPP Deep Learning with Python and PyTorch focuses on practical skills in deep learning Python programming with PyTorch library organized within the following structure, encompassing comprehensive and structured EITCI certification curriculum self-learning materials supported by referenced open-access video didactic content as a basis for preparation towards earning this EITC Certification by passing a corresponding examination.
Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, machine vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance.
Python is an interpreted, high-level and general-purpose programming language. Python’s design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects. Python is often described as a “batteries included” language due to its comprehensive standard library. Python is commonly used in artificial intelligence projects and machine learning projects with the help of libraries like TensorFlow, Keras, Pytorch and Scikit-learn.
Python is dynamically-typed (executing at runtime many common programming behaviours that static programming languages perform during compilation) and garbage-collected (with automatic memory management). It supports multiple programming paradigms, including structured (particularly, procedural), object-oriented and functional programming. It was created in the late 1980s, and first released in 1991, by Guido van Rossum as a successor to the ABC programming language. Python 2.0, released in 2000, introduced new features, such as list comprehensions, and a garbage collection system with reference counting, and was discontinued with version 2.7 in 2020. Python 3.0, released in 2008, was a major revision of the language that is not completely backward-compatible and much Python 2 code does not run unmodified on Python 3. With Python 2’s end-of-life (and pip having dropped support in 2021), only Python 3.6.x and later are supported, with older versions still supporting e.g. Windows 7 (and old installers not restricted to 64-bit Windows).
Python interpreters are supported for mainstream operating systems and available for a few more (and in the past supported many more). A global community of programmers develops and maintains CPython, a free and open-source reference implementation. A non-profit organization, the Python Software Foundation, manages and directs resources for Python and CPython development.
As of January 2021, Python ranks third in TIOBE’s index of most popular programming languages, behind C and Java, having previously gained second place and their award for the most popularity gain for 2020. It was selected Programming Language of the Year in 2007, 2010, and 2018.
An empirical study found that scripting languages, such as Python, are more productive than conventional languages, such as C and Java, for programming problems involving string manipulation and search in a dictionary, and determined that memory consumption was often “better than Java and not much worse than C or C++”. Large organizations that use Python include i.a. Wikipedia, Google, Yahoo!, CERN, NASA, Facebook, Amazon, Instagram.
Beyond its artificial intelligence applications, Python, as a scripting language with modular architecture, simple syntax and rich text processing tools, is often used for natural language processing.
PyTorch is an open source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, primarily developed by Facebook’s AI Research lab (FAIR). It is free and open-source software released under the Modified BSD license. Although the Python interface is more polished and the primary focus of development, PyTorch also has a C++ interface. A number of pieces of Deep Learning software are built on top of PyTorch, including Tesla Autopilot, Uber’s Pyro, HuggingFace’s Transformers, PyTorch Lightning, and Catalyst.
- Tensor computing (like NumPy) with strong acceleration via graphics processing units (GPU)
- Deep neural networks built on a tape-based automatic (computational) differentiation system
Facebook operates both PyTorch and Convolutional Architecture for Fast Feature Embedding (Caffe2), but models defined by the two frameworks were mutually incompatible. The Open Neural Network Exchange (ONNX) project was created by Facebook and Microsoft in September 2017 for converting models between frameworks. Caffe2 was merged into PyTorch at the end of March 2018.
PyTorch defines a class called Tensor (torch.Tensor) to store and operate on homogeneous multidimensional rectangular arrays of numbers. PyTorch Tensors are similar to NumPy Arrays, but can also be operated on a CUDA-capable Nvidia GPU. PyTorch supports various sub-types of Tensors.
There are few important modules for Pytorch. These include:
- Autograd module: PyTorch uses a method called automatic differentiation. A recorder records what operations have performed, and then it replays it backward to compute the gradients. This method is especially powerful when building neural networks to save time on one epoch by calculating differentiation of the parameters at the forward pass.
- Optim module: torch.optim is a module that implements various optimization algorithms used for building neural networks. Most of the commonly used methods are already supported, so there is no need to build them from scratch.
- nn module: PyTorch autograd makes it easy to define computational graphs and take gradients, but raw autograd can be a bit too low-level for defining complex neural networks. This is where the nn module can help.
To acquaint yourself in-detail with the certification curriculum you can expand and analyze the table below.
The EITC/AI/DLPP Deep Learning with Python and PyTorch Certification Curriculum references open-access didactic materials in a video form by Harrison Kinsley. Learning process is divided into a step-by-step structure (programmes -> lessons -> topics) covering relevant curriculum parts. Participants can access answers and ask more relevant questions in the Questions and answers section of the e-learning interface under currently progressed EITC programme curriculum topic. Direct and unlimited consultancy with domain experts is also accessible via the platform integrated online messaging system, as well as through the contact form.
For details on the Certification procedure check How it Works.
Download the complete offline self-learning preparatory materials for the EITC/AI/DLPP Deep Learning with Python and PyTorch programme in a PDF file
EITC/AI/DLPP preparatory materials – standard version