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Eğitim İçeriği
Part 1 – Deep Learning and DNN Concepts
Introduction to AI, Machine Learning & Deep Learning
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History, basic concepts, and common applications of artificial intelligence, moving beyond the common fantasies surrounding this field.
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Collective Intelligence: aggregating knowledge shared by many virtual agents.
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Genetic algorithms: evolving a population of virtual agents through selection.
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Usual Learning Machine: definition.
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Types of tasks: supervised learning, unsupervised learning, reinforcement learning.
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Types of actions: classification, regression, clustering, density estimation, dimensionality reduction.
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Examples of Machine Learning algorithms: Linear regression, Naive Bayes, Random Tree.
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Machine learning VS Deep Learning: problems where Machine Learning remains the state of the art (Random Forests & XGBoost).
Basic Concepts of a Neural Network (Application: multi-layer perceptron)
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Reminder of mathematical foundations.
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Definition of a neural network: classical architecture, activation functions.
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Weighting of previous activations, network depth.
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Definition of neural network learning: cost functions, back-propagation, stochastic gradient descent, maximum likelihood.
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Modeling a neural network: modeling input and output data according to the problem type (regression, classification, etc.). The curse of dimensionality.
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Distinction between multi-feature data and signals. Choosing a cost function based on the data.
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Approximating a function with a neural network: presentation and examples.
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Approximating a distribution with a neural network: presentation and examples.
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Data Augmentation: how to balance a dataset.
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Generalizing the results of a neural network.
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Initializing and regularizing a neural network: L1 / L2 regularization, BatchNormalization.
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Optimization and convergence algorithms.
Standard ML / DL Tools
A simple presentation outlining advantages, disadvantages, position within the ecosystem, and use cases will be provided.
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Data management tools: Apache Spark, Apache Hadoop Tools.
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Machine Learning: Numpy, Scipy, Sci-kit.
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DL high-level frameworks: PyTorch, Keras, Lasagne.
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Low-level DL frameworks: Theano, Torch, Caffe, Tensorflow.
Convolutional Neural Networks (CNN).
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Presentation of CNNs: fundamental principles and applications.
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Basic operation of a CNN: convolutional layer, use of a kernel.
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Padding & stride, feature map generation, pooling layers. Extensions 1D, 2D, and 3D.
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Presentation of different CNN architectures that have advanced the state of the art in classification.
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Images: LeNet, VGG Networks, Network in Network, Inception, Resnet. Presentation of innovations brought about by each architecture and their broader applications (Convolution1x1 or residual connections).
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Use of an attention model.
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Application to a common classification case (text or image).
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CNNs for generation: super-resolution, pixel-to-pixel segmentation. Presentation of
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Main strategies for increasing feature maps for image generation.
Recurrent Neural Networks (RNN).
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Presentation of RNNs: fundamental principles and applications.
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Basic operation of the RNN: hidden activation, back propagation through time, unfolded version.
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Evolutions towards Gated Recurrent Units (GRUs) and LSTM (Long Short Term Memory).
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Presentation of the different states and the evolutions brought about by these architectures.
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Convergence and vanishing gradient problems.
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Classical architectures: Prediction of a temporal series, classification…
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RNN Encoder Decoder type architecture. Use of an attention model.
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NLP applications: word / character encoding, translation.
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Video Applications: prediction of the next generated image of a video sequence.
Generational models: Variation AutoEncoder (VAE) and Generative Adversarial Networks (GAN).
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Presentation of generational models, link with CNNs.
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Auto-encoder: dimensionality reduction and limited generation.
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Variation Auto-encoder: generational model and approximation of a given distribution. Definition and use of latent space. Reparameterization trick. Applications and limits observed.
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Generative Adversarial Networks: Fundamentals.
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Dual Network Architecture (Generator and discriminator) with alternate learning, available cost functions.
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Convergence of a GAN and difficulties encountered.
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Improved convergence: Wasserstein GAN, Began. Earth Moving Distance.
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Applications for image or photograph generation, text generation, super-resolution.
Deep Reinforcement Learning.
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Presentation of reinforcement learning: controlling an agent in a defined environment.
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By a state and possible actions.
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Using a neural network to approximate the state function.
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Deep Q Learning: experience replay, and application to controlling a video game.
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Optimizing the learning policy. On-policy && off-policy. Actor-critic architecture. A3C.
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Applications: controlling a single video game or a digital system.
Part 2 – Theano for Deep Learning
Theano Basics
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Introduction.
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Installation and Configuration.
Theano Functions
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inputs, outputs, updates, givens.
Training and Optimization of a neural network using Theano
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Neural Network Modeling.
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Logistic Regression.
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Hidden Layers.
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Training a network.
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Computing and Classification.
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Optimization.
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Log Loss.
Testing the model
Part 3 – DNN using Tensorflow
TensorFlow Basics
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Creation, Initializing, Saving, and Restoring TensorFlow variables.
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Feeding, Reading and Preloading TensorFlow Data.
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How to use TensorFlow infrastructure to train models at scale.
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Visualizing and Evaluating models with TensorBoard.
TensorFlow Mechanics
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Prepare the Data.
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Download.
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Inputs and Placeholders.
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Build the Graphs.
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Inference.
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Loss.
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Training.
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Train the Model.
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The Graph.
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The Session.
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Train Loop.
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Evaluate the Model.
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Build the Eval Graph.
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Eval Output.
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The Perceptron
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Activation functions.
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The perceptron learning algorithm.
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Binary classification with the perceptron.
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Document classification with the perceptron.
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Limitations of the perceptron.
From the Perceptron to Support Vector Machines
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Kernels and the kernel trick.
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Maximum margin classification and support vectors.
Artificial Neural Networks
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Nonlinear decision boundaries.
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Feedforward and feedback artificial neural networks.
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Multilayer perceptrons.
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Minimizing the cost function.
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Forward propagation.
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Back propagation.
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Improving the way neural networks learn.
Convolutional Neural Networks
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Goals.
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Model Architecture.
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Principles.
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Code Organization.
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Launching and Training the Model.
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Evaluating a Model.
Basic Introductions to be given to the below modules (Brief Introduction to be provided based on time availability):
Tensorflow - Advanced Usage
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Threading and Queues.
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Distributed TensorFlow.
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Writing Documentation and Sharing your Model.
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Customizing Data Readers.
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Manipulating TensorFlow Model Files.
TensorFlow Serving
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Introduction.
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Basic Serving Tutorial.
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Advanced Serving Tutorial.
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Serving Inception Model Tutorial.
Kurs İçin Gerekli Önbilgiler
Fizik, matematik ve programlama alanlarında geçmişe sahip olmak. Görüntü işleme faaliyetlerine katılım.
Katılımcıların makine öğrenimi kavramları hakkında önceden bilgi sahibi olması ve Python programlama ve kütüphaneleri üzerinde çalışmış olması gerekmektedir.
35 Saat
Danışanlarımızın Yorumları (5)
Hunter is fabulous, very engaging, extremely knowledgeable and personable. Very well done.
Rick Johnson - Laramie County Community College
Eğitim - Artificial Intelligence (AI) Overview
The trainer explained the content well and was engaging throughout. He stopped to ask questions and let us come to our own solutions in some practical sessions. He also tailored the course well for our needs.
Robert Baker
Eğitim - Deep Learning with TensorFlow 2.0
Tomasz really know the information well and the course was well paced.
Raju Krishnamurthy - Google
Eğitim - TensorFlow Extended (TFX)
Organization, adhering to the proposed agenda, the trainer's vast knowledge in this subject
Ali Kattan - TWPI
Eğitim - Natural Language Processing with TensorFlow
Very updated approach or CPI (tensor flow, era, learn) to do machine learning.
