Final Exam: DL Programmer
- 1 video | 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
distinguish been input, output, and hidden layers in a neural networkrecognize the different types of neural network computational modelsdescribe ResNet layers and blocksimplement long short-term memory using TensorFlowcompare the supervised and unsupervised learning methods of artificial neural networkslist neural network algorithms that can be used to solve complex problems across domainsrecall the essential Hyperparameters that are applied on Convolutional networks for optimization and model refinementrecall the approaches of identifying overfitting scenarios and preventing overfitting using Regularization techniquesdescribe gradient descent and list its prominent variantsrecognize the need for Activation layer in Convolutional Neural networks and compare the prominent Activation functions for Deep Neural networksidentify the need for Activation layer in Convolutional Neural networks and compare the prominent Activation functions for Deep Neural networksdefine and classify Activation functions and provide a comparative analysis with the pros and cons of the different types of Activation functionsrecognize the Machine learning problems that we can address using Hyperparameters along with the various Hyperparameter tuning methods and the problems associated with Hyperparameter optimizationdefine multilayer perceptrons and illustrate the algorithmic difference from single layer perceptronsdescribe functions in calculusdemonstrate how to test multiple models and select the right model using Scikit-learndescribe the approach of creating Deep learning network models along with the steps involved in optimizing the networksdescribe shared parameters and spatial in a convolutional neural network (CNN)demonstrate how to build a Convolutional Neural network for Image classification using Pythondescribe the concept of Scaling data and list the prominent data scaling methodsdefine semantic segmentation and its implementation using Texton Forest and random-based classifierdefine the concepts of variance, covariance and random vectorslist the essential clustering techniques that can be applied on artificial neural networkuse backpropagation and Keras to implement multi-layer perceptron or neural netidentify and illustrate the use of learning rates to optimize deep learningbuild a recurrent neural network using PyTorch and Google Colabdescribe vanishing gradient problem implementation approachesdescribe the regularization techniques used in deep neural networkdevelop Convolutional Neural network models from the scratch for Object Photo classification using Python and Kerasbuild neural networks using PyTorch
specify approaches that can be used to implement predictions with neural networksrecognize the need for gradient optimization in neural networksimplement convolutional neural networks (CNNs) using PyTorchwork with Hyperparameters using Keras and TensorFlow to derive optimized Convolutional network modelsdemonstrate how to select hyperparameters and tune for dense networks using Hyperasdefine the concept of the Edge Detection method and list the common algorithms that are used for Edge detectionrecognize the various approaches of improving the performance of Machine learning using data, algorithm, algorithm tuning and Ensemblesidentify the different types of learning rules that can be applied in neural networksbuild deep learning language models using Kerasdescribe the purpose of a training function in an artificial neural networkcalculate Loss function and score using Pythonrecognize 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 networksimplement backpropagation using Python to train artificial neural networksrecognize the differences between the non-linear activation functionsrecognize the role of Pooling layer in Convolutional networks along with the various operations and functions that we can apply on the layerdescribe the temporal and heterogeneous approaches of optimizing predictionsimplement recurrent neural network using Python and TensorFlowlist activation mechanisms used in the implementation of neural networksdescribe sequence modeling as it pertains to language modelsimplement calculus, derivatives, and integrals using Pythonimplement the artificial neural network training process using Pythonlist features and characteristics of gated recurrent units (GRUs)demonstrate the implementation of differentiation and integration in Rwork with threshold functions in neural networksdescribe the iterative workflow for Machine learning problems with focus on essential measures and evaluation protocolsrecognize the importance of linear algebra in machine learningdefine and illustrate the use of learning rates to optimize deep learningrecall the prominent Optimizer algorithms along with their properties that can be applied for optimizationrecognize the involvement of Maths in Convolutional Neural networks and recall the essential rules that are applied on Filters and Channel detectionrecall the algorithms that can be used to train neural networks
IN THIS COURSE
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
Skillsoft is providing you the opportunity to earn a digital badge upon successful completion on some of our courses, which can be shared on any social network or business platform.Digital badges are yours to keep, forever.
YOU MIGHT ALSO LIKE
Course Final Exam: FSD Developer