Final Exam: DL Programmer
- 1 Video | 30m 32s
- Includes Assessment
- Earns a Badge
Final Exam: DL Programmer will test your knowledge and application of the topics presented throughout the DL Programmer track of the Skillsoft Aspire ML Programmer to ML Architect Journey.
WHAT YOU WILL LEARN
build a recurrent neural network using PyTorch and Google Colabbuild deep learning language models using Kerasbuild neural networks using PyTorchcalculate Loss function and score using Pythoncompare the supervised and unsupervised learning methods of artificial neural networksdefine and classify Activation functions and provide a comparative analysis with the pros and cons of the different types of Activation functionsdefine and illustrate the use of learning rates to optimize deep learningdefine multilayer perceptrons and illustrate the algorithmic difference from single layer perceptronsdefine semantic segmentation and its implementation using Texton Forest and random-based classifierdefine the concept of the Edge Detection method and list the common algorithms that are used for Edge detectiondefine the concepts of variance, covariance and random vectorsdemonstrate how to build a Convolutional Neural network for Image classification using Pythondemonstrate how to select hyperparameters and tune for dense networks using Hyperasdemonstrate how to test multiple models and select the right model using Scikit-learndemonstrate the implementation of differentiation and integration in Rdescribe functions in calculusdescribe gradient descent and list its prominent variantsdescribe ResNet layers and blocksdescribe sequence modeling as it pertains to language modelsdescribe shared parameters and spatial in a convolutional neural network (CNN)describe the approach of creating Deep learning network models along with the steps involved in optimizing the networksdescribe the concept of Scaling data and list the prominent data scaling methodsdescribe the iterative workflow for Machine learning problems with focus on essential measures and evaluation protocolsdescribe the purpose of a training function in an artificial neural networkdescribe the regularization techniques used in deep neural networkdescribe the temporal and heterogeneous approaches of optimizing predictionsdescribe vanishing gradient problem implementation approachesdevelop Convolutional Neural network models from the scratch for Object Photo classification using Python and Kerasdistinguish been input, output, and hidden layers in a neural networkidentify and illustrate the use of learning rates to optimize deep learning
identify the different types of learning rules that can be applied in neural networksidentify the need for Activation layer in Convolutional Neural networks and compare the prominent Activation functions for Deep Neural networksimplement backpropagation using Python to train artificial neural networksimplement calculus, derivatives, and integrals using Pythonimplement convolutional neural networks (CNNs) using PyTorchimplement long short-term memory using TensorFlowimplement recurrent neural network using Python and TensorFlowimplement the artificial neural network training process using Pythonlist activation mechanisms used in the implementation of neural networkslist features and characteristics of gated recurrent units (GRUs)list neural network algorithms that can be used to solve complex problems across domainslist the essential clustering techniques that can be applied on artificial neural networkrecall the algorithms that can be used to train neural networksrecall the approaches of identifying overfitting scenarios and preventing overfitting using Regularization techniquesrecall the essential Hyperparameters that are applied on Convolutional networks for optimization and model refinementrecall the prominent Optimizer algorithms along with their properties that can be applied for optimizationrecognize the differences between the non-linear activation functionsrecognize the different types of neural network computational modelsrecognize the importance of linear algebra in machine learningrecognize the involvement of Maths in Convolutional Neural networks and recall the essential rules that are applied on Filters and Channel detectionrecognize the limitations of Sigmoid and Tanh and describe how they can be resolved using ReLU along with the significant benefits afforded by ReLU when applied in Convolutional networksrecognize the Machine learning problems that we can address using Hyperparameters along with the various Hyperparameter tuning methods and the problems associated with Hyperparameter optimizationrecognize the need for Activation layer in Convolutional Neural networks and compare the prominent Activation functions for Deep Neural networksrecognize the need for gradient optimization in neural networksrecognize the role of Pooling layer in Convolutional networks along with the various operations and functions that we can apply on the layerrecognize the various approaches of improving the performance of Machine learning using data, algorithm, algorithm tuning and Ensemblesspecify approaches that can be used to implement predictions with neural networksuse backpropagation and Keras to implement multi-layer perceptron or neural network with Hyperparameters using Keras and TensorFlow to derive optimized Convolutional network modelswork with threshold functions in neural networks
IN THIS COURSE
1.DL Programmer33sUP NEXT
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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