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Deep learning is an AI function that mimics the workings of the human brain in processing data for use in detecting objects, recognizing speech, translating languages, and making decisions. Deep learning AI is able to learn without human supervision, drawing from data that is both unstructured and unlabeled. Also known as deep neural learning or deep neural network.
Duration of Training : 100 hrs
Batch type : weekdays /weekends/ Customized Batches
Mode of Training: Offline / Online / Corporate Training
Projects Given : 2 Projects minimum
Trainer Profile : Experienced Faculty from IT Industry
Projects | Assignment | Scenarios and Used Case Studies
What is deep learning examples?
Deep learning utilizes both structured and unstructured data for training. Practical examples of deep learning are Virtual assistants, vision for driverless cars, money laundering, face recognition and many more.
Where is deep learning used?
Deep learning applications are used in industries from automated driving to medical devices. Automated Driving: Automotive researchers are using deep learning to automatically detect objects such as stop signs and traffic lights. In addition, deep learning is used to detect pedestrians, which helps decrease accidents.
1.Introduction to Github & Kaggle
2.Introduction to Machine Learning Concepts
3.Mathematics of Artificial Neural Network.
4.Single neuron prediction model.
1.Introduction to Google’s TensorFlow Framework for Deep Learning
2.Data types in TF, key data transformation Methods.
3.Implement TensorFlow data pipeline using Tfrecords and tf.data methods
1.Construct a Deep Learning Model to predict an Image.
2.Details of Sequential vs functional API of TF Keras implementation.
3.Hyper Parameter tunning of model.
1.Convolution Neural Network
2.Advanced CNN Networks – AlexNet, Residual Networks (ResNet)
3.Implement ResNET model in Google Colab.
1.Introduction to TensorFlow Hub
1.Open Source labelling tools for custom data annotation.
2.Fine tune pre trained ResNet & Inception V4 models.
1.TF’s Object Detection API
2.FasterRCNN algorithm.
3.MaskRCNN for image segmentation
1.Introduction to Word Embeddings
2.Recurrent Neural Networks (RNN)
3.LSTM / Bi LSTM & GRU Networks
1.Named Entity Extraction using spaCy library
1.Construct a custom NER model using BiLSTM netowrk
2.Hyper Parameter Tunning of BiLSTM and spaCy’s model
1.Introduction to BERT architecture
2.Introduction to Hugging face’s BERT methods
3.Fine Tune a Question & Answering Model on Custom data set
Course completion certificate and Global Certifications are part of our all Master Program
Course completion certificate and Global Certifications are part of our all Master Program
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