AI & Deep Learning with TensorFlow
Pinnacledu’s Deep Learning in TensorFlow with Python Certification Training is curated by industry professionals as per the industry requirements & demands. You will master the concepts such as SoftMax function, Autoencoder Neural Networks, Restricted Boltzmann Machine (RBM) and work with libraries like Keras & TFLearn. The course has been specially curated by industry experts with real-time case studies.
Course Features
- Lectures 76
- Quizzes 0
- Duration 50 hours
- Skill level All levels
- Language English
- Students 28725
- Assessments Yes
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Introduction to Deep Learning
Learning Objectives: In this module, you’ll get an introduction to Deep Learning and understand how Deep Learning solves problems which Machine Learning cannot. Understand fundamentals of Machine Learning and relevant topics of Linear Algebra and Statistics.
- Deep Learning: A revolution in Artificial Intelligence
- Limitations of Machine Learning
- What is Deep Learning?
- Advantage of Deep Learning over Machine learning
- 3 Reasons to go for Deep Learning
- Real-Life use cases of Deep Learning
- Review of Machine Learning: Regression, Classification, Clustering, Reinforcement Learning, Underfitting and Overfitting, Optimization
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Understanding Neural Networks with TensorFlow
Learning Objectives: In this module, you’ll get an introduction to Neural Networks and understand it’s working i.e. how it is trained, what are the various parameters considered for its training and the activation functions that are applied.
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Deep dive into Neural Networks with TensorFlow
Learning Objectives: In this module, you’ll understand backpropagation algorithm which is used for training Deep Networks. You will know how Deep Learning uses neural network and backpropagation to solve the problems that Machine Learning cannot.
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Master Deep Networks
Learning Objectives: In this module, you’ll get started with the TensorFlow framework. You will understand how it works, its various data types & functionalities. In addition, you will create an image classification model.
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Convolutional Neural Networks (CNN)
Learning Objectives: In this module, you’ll understand convolutional neural networks and its applications. You will learn the working of CNN, and create a CNN model to solve a problem.
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Recurrent Neural Networks (RNN)
Learning Objectives: In this module, you’ll understand Recurrent Neural Networks and its applications. You will understand the working of RNN, how LSTM are used in RNN, what is Recursive Neural Tensor Network Theory, and finally you will learn to create a RNN model.
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Restricted Boltzmann Machine (RBM) and Autoencoders
Learning Objectives: In this module, you’ll understand RBM & Autoencoders along with their applications. You will understand the working of RBM & Autoencoders, illustrate Collaborative Filtering using RBM and understand what are Deep Belief Networks.
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Keras API
Learning Objectives: In this module, you’ll understand how to use Keras API for implementing Neural Networks. The goal is to understand various functions and features that Keras provides to make the task of neural network implementation easy.
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TFLearn API
Learning Objectives: In this module, you’ll understand how to use TFLearn API for implementing Neural Networks. The goal is to understand various functions and features that TFLearn provides to make the task of neural network implementation easy.
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In-Class Project
Learning Objectives: In this module, you should learn how to approach and implement a project end to end. The instructor will share his industry experience and related insights that will help you kickstart your career in this domain. In addition, we will be having a QA and doubt clearing session for you.
