Deep Learning Training in Nagpur with real time Practice and live project
C/C++
JAVA/JEE
Microsoft .NET
Deep Learning Training in Nagpur with Keras & TensorFlow
Recommended Technology
Python
R programming
Artificial Intelligence
Machine Learning
Deep Learning Training in Nagpur Course Overview
Deep Learning Training in Nagpur will transform you into an expert in profound learning approach using TensorFlow, the open-source programming library designed to conduct machine learning & deep neural network research. With our profound adapting Training, you’ll ace profound learning and TensorFlow ideas, figure out how to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and set yourself up for your new job as a profound learning researcher
Prerequisites for Deep Learning Training in Nagpur
To attend this course you must have hands-on, the experience of object-oriented programming, ideally, Python and experience in other languages, including R, have been very successful. If you are not fully familiar with such topics, please make sure you have some practice before the course starts. You may also like
Training from professional Deep Learning Engineer
Introduction to Deep Learning
- An Introduction to Neural Networks and Deep Learning
- Course Overview
- Interactive Visualization of an Artificial Neural Network
- Hardware Options for Deep Learning, including How to Build a Deep Learning Overview
- Running a TensorFlow Jupyter Notebook within a Docker Container
- A Shallow Artificial Neural Network
How Deep Learning Works
- Essential Theory I: Neural Units
- Interactive Visualization of Neural Units
- Essential Theory II: Cost Functions, Gradient Descent, and Backpropagation
- Interactive Visualization of a Deep Neural Network
- An Intermediate Neural Network
- Data Sets for Deep Learning
Building and Training a Deep Learning Network
- Review Content and Take-Home Exercises
- Essential Theory III: Weight Initialization and Mini-Batches
- Essential Theory IV: Unstable Gradients and Avoiding Overfitting
- A Deep Neural Network
- TensorBoard and the Interpretation of Model Outputs
Machine Vision
- Introduction to Convolutional Neural Networks for Visual Recognition
- Classic ConvNet Architecture I: LeNet-5
- Classic ConvNet Architecture II: AlexNet
- Transfer Learning
- The Families of Deep Neural Nets and their Applications
TensorFlow
- Reviewing Content and Take-Home Exercises
- Comparison of the Leading Deep Learning Libraries
- TensorFlow Graphs
- Neurons in TensorFlow
- Fitting Models in TensorFlow
Deep Learning with TensorFlow
- Deep Dense Nets in TensorFlow
- Deep Convolutional Nets in TensorFlow
- TensorBoard Best-Practices
- Improving Model Performance
- Tuning Hyperparameters
Natural Language Processing
- Reviewing Content and Take-Home Exercises
- Word Vectors: word2vec and Vector-Space Embedding
- Recurrent Neural Networks
- Long Short-Term Memory Units
- Sentiment Analysis
Time Series Analysis
- Machine Translation: Sequence-to-Sequence Models and Attention
- Neural Network Architectures for Question-Answering
- Forecasting with Financial Time Series Data
Generative Adversarial Networks
- Reviewing Content and Take-Home Exercises
- Applications of GANs
- Essential Theory of GANs
- Implementations of GANs
Reinforcement Learning
- Applications of Reinforcement Learning
- OpenAI Gym
- Essential Theory of Reinforcement Learning
- Implementations of Reinforcement Learning
- Jeanne Calment and Your Role in the AI Revolution
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Why Chooce Us
- Training from professional xamarin developers
- 10 years of experience
- Training and Internship combined
- Real-Time Development experience
- Fully Equapaid Lab, With AC & WIFI Internet available
- Support and Careers Advice
- We Offer Quality Training
- and so much more…