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EITC/AI/DLTF Deep Learning with TensorFlow

by admin / Wednesday, 03 February 2021 / Published in Uncategorized
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EITC/AI/DLTF Deep Learning with TensorFlow is the European IT Certification programme on the fundamentals of programming deep learning in Python with Google TensorFlow machine learning library.

The curriculum of the EITC/AI/DLTF Deep Learning with TensorFlow focuses on practical skills in deep learning Python programming with Google TensorFlow library organized within the following structure, encompassing comprehensive video didactic content as a reference for this EITC Certification.

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.

Artificial neural networks (ANNs) were inspired by information processing and distributed communication nodes in biological systems. The adjective “deep” in deep learning refers to the use of multiple layers in the network. Early work showed that a linear perceptron cannot be a universal classifier, and then that a network with a nonpolynomial activation function with one hidden layer of unbounded width can on the other hand so be. Deep learning is a modern variation which is concerned with an unbounded number of layers of bounded size, which permits practical application and optimized implementation, while retaining theoretical universality under mild conditions. In deep learning the layers are also permitted to be heterogeneous and to deviate widely from biologically informed connectionist models, for the sake of efficiency, trainability and understandability, whence the “structured” part.

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.

TensorFlow is a free and open-source software library for machine learning. It can be used across a range of tasks but has a particular focus on training and inference of deep neural networks. It is a symbolic math library based on dataflow and differentiable programming. It is used for both research and production at Google.

TensorFlow was developed by the Google Brain team for internal Google use. It was released under the Apache License 2.0 in 2015.

Starting in 2011, Google Brain built DistBelief as a proprietary machine learning system based on deep learning neural networks. Its use grew rapidly across diverse Alphabet companies in both research and commercial applications. Google assigned multiple computer scientists, including Jeff Dean, to simplify and refactor the codebase of DistBelief into a faster, more robust application-grade library, which became TensorFlow. In 2009, the team, led by Geoffrey Hinton, had implemented generalized backpropagation and other improvements which allowed generation of neural networks with substantially higher accuracy, for instance a 25% reduction in errors in speech recognition.

TensorFlow is Google Brain’s second-generation system. Version 1.0.0 was released on February 11, 2017. While the reference implementation runs on single devices, TensorFlow can run on multiple CPUs and GPUs (with optional CUDA and SYCL extensions for general-purpose computing on graphics processing units). TensorFlow is available on 64-bit Linux, macOS, Windows, and mobile computing platforms including Android and iOS. Its flexible architecture allows for the easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. TensorFlow computations are expressed as stateful dataflow graphs. The name TensorFlow derives from the operations that such neural networks perform on multidimensional data arrays, which are referred to as tensors. During the Google I/O Conference in June 2016, Jeff Dean stated that 1,500 repositories on GitHub mentioned TensorFlow, of which only 5 were from Google. In December 2017, developers from Google, Cisco, RedHat, CoreOS, and CaiCloud introduced Kubeflow at a conference. Kubeflow allows operation and deployment of TensorFlow on Kubernetes. In March 2018, Google announced TensorFlow.js version 1.0 for machine learning in JavaScript. In Jan 2019, Google announced TensorFlow 2.0. It became officially available in Sep 2019. In May 2019, Google announced TensorFlow Graphics for deep learning in computer graphics.

To acquaint yourself in-detail with the certification curriculum you can expand and analyze the table below.

The EITC/AI/DLTF Deep Learning with TensorFlow 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. Unlimited consultancy with domain experts are also provided.
For details on the Certification procedure check How it Works.

Curriculum Reference Resources

Google TensorFlow
https://www.tensorflow.org/

Google TensorFlow Learning Resources
https://www.tensorflow.org/learn/

TensorFlow API Documentation
https://www.tensorflow.org/api_docs/

TensorFlow Models and Datasets
https://www.tensorflow.org/resources/models-datasets/

TensorFlow Community
https://www.tensorflow.org/community/

Google Cloud AI Platform Training with TensorFlow
https://cloud.google.com/ai-platform/training/docs/tensorflow-2/

Certification Programme Curriculum

Expand All
Introduction 1 Topic
Expand
Lesson Content
0% Complete 0/1 Steps
Introduction to deep learning with neural networks and TensorFlow
TensorFlow 10 Topics
Expand
Lesson Content
0% Complete 0/10 Steps
Installing TensorFlow
TensorFlow basics
Neural network model
Running the network
Processing data
Preprocessing conitnued
Training and testing on data
Using more data
Installing the GPU version of TensorFlow for making use of a CUDA GPU
Installing CPU and GPU TensorFlow on Windows
Recurrent neural networks in TensorFlow 2 Topics
Expand
Lesson Content
0% Complete 0/2 Steps
Recurrent neural networks (RNN)
RNN example in Tensorflow
Convolutional neural networks in TensorFlow 2 Topics
Expand
Lesson Content
0% Complete 0/2 Steps
Convolutional neural networks basics
Convolutional neural networks with TensorFlow
TensorFlow Deep Learning Library 1 Topic
Expand
Lesson Content
0% Complete 0/1 Steps
TFLearn
Training a neural network to play a game with TensorFlow and Open AI 4 Topics
Expand
Lesson Content
0% Complete 0/4 Steps
Introduction
Training data
Training model
Testing network
Using convolutional neural network to identify dogs vs cats 4 Topics
Expand
Lesson Content
0% Complete 0/4 Steps
Introduction and preprocessing
Building the network
Training the network
Using the network
3D convolutional neural network with Kaggle lung cancer detection competiton 6 Topics
Expand
Lesson Content
0% Complete 0/6 Steps
Introduction
Reading files
Visualizing
Resizing data
Preprocessing data
Running the network
Deep learning in the browser with TensorFlow.js 4 Topics
Expand
Lesson Content
0% Complete 0/4 Steps
Introduction
Basic TensorFlow.js web application
AI Pong in TensorFlow.js
Training model in Python and loading into TensorFlow.js
Creating a chatbot with deep learning, Python, and TensorFlow 9 Topics
Expand
Lesson Content
0% Complete 0/9 Steps
Introduction
Data structure
Buffering dataset
Determining insert
Building database
Database to training data
Training a model
NMT concepts and parameters
Interacting with the chatbot
EITC/AI/DLTF Deep Learning with TensorFlow
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Programme Home Expand All
Introduction
1 Topic
Introduction to deep learning with neural networks and TensorFlow
TensorFlow
10 Topics
Installing TensorFlow
TensorFlow basics
Neural network model
Running the network
Processing data
Preprocessing conitnued
Training and testing on data
Using more data
Installing the GPU version of TensorFlow for making use of a CUDA GPU
Installing CPU and GPU TensorFlow on Windows
Recurrent neural networks in TensorFlow
2 Topics
Recurrent neural networks (RNN)
RNN example in Tensorflow
Convolutional neural networks in TensorFlow
2 Topics
Convolutional neural networks basics
Convolutional neural networks with TensorFlow
TensorFlow Deep Learning Library
1 Topic
TFLearn
Training a neural network to play a game with TensorFlow and Open AI
4 Topics
Introduction
Training data
Training model
Testing network
Using convolutional neural network to identify dogs vs cats
4 Topics
Introduction and preprocessing
Building the network
Training the network
Using the network
3D convolutional neural network with Kaggle lung cancer detection competiton
6 Topics
Introduction
Reading files
Visualizing
Resizing data
Preprocessing data
Running the network
Deep learning in the browser with TensorFlow.js
4 Topics
Introduction
Basic TensorFlow.js web application
AI Pong in TensorFlow.js
Training model in Python and loading into TensorFlow.js
Creating a chatbot with deep learning, Python, and TensorFlow
9 Topics
Introduction
Data structure
Buffering dataset
Determining insert
Building database
Database to training data
Training a model
NMT concepts and parameters
Interacting with the chatbot
EITC/AI/DLTF Deep Learning with TensorFlow

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