Training Highlights

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What placement assistance will you receive?
Free Placement Preparation Training
Access to curated Internships & Jobs
Top performers will be highlighted in their internship & job applications

What will be the training syllabus?
Course Curriculum
Module 1 : Introduction to TensorFlow
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Overview of TensorFlow and its features
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Setting up a TensorFlow environment
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Basic concepts in machine learning and deep learning
Module 2 : TensorFlow Basics
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Working with tensors and operations in TensorFlow
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Building and training simple machine learning models
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Saving and restoring models in TensorFlow
Module 3: Neural Networks
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Introduction to neural networks and their architecture
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Building and training neural networks in TensorFlow
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Techniques for improving the performance of neural networks (e.g. regularization, dropout)
Module 4: Convolutional Neural Networks (CNNs)
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Introduction to CNNs and their use in image classification
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Building and training CNNs in TensorFlow
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Techniques for improving the performance of CNNs (e.g. data augmentation, transfer learning)
Modle 5 : Recurrent Neural Networks (RNNs)
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Introduction to RNNs and their use in sequential data processing
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Building and training RNNs in TensorFlow
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Techniques for improving the performance of RNNs (e.g. long short-term memory, attention mechanisms)
Module 6 : Advanced Topics in TensorFlow
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Using TensorFlow’s advanced features and APIs (e.g. Estimators, Keras, TensorFlow Serving)
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Using TensorFlow in distributed and production environments
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Working with large datasets and distributed training
Module 7 : Applications of TensorFlow
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Examples of using TensorFlow in real-world applications (e.g. image classification, natural language processing, time series analysis)
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Best practices for implementing and deploying TensorFlow models in production