About Deep Learning Online Training
Deep learning training gives you an in-depth understanding of the architecture of TensorFlow Core, API layers, and therefore the use cases. Master unsupervised learning models, deep learning models and more. Right from installing and configuring TensorFlow, importing data, simple models to develop complex layered models and architectures to crunch huge data sets leveraging the distributed, robust and scalable machine learning framework from Google.
Learn to implement Keras on top of TensorFlow to experiment with deep neural networks and tune machine learning models to supply more successful results with our deep learning with TensorFlow course.
Work on differing types of Deep Architectures: Convolutional Networks, Recurrent Networks, and Autoencoders, and further get conversant in the advanced concepts of tongue Processing. Also, gain practical exposure on text to speech processing.
Our cloud labs comprise guided exercises practice building handwritten digit recognition, deep learning, convolution and time-series models of Neural Networks. Gain hands-on experience by working with real-time uses cases and data sets using various neural specification , suitable to different industry domains and supply solutions.
Lead TensorFlow based AI projects together with your teams trained in our TensorFlow course.
Prerequisites For Learning Deep Learning Online Training
- Basic Programming knowledge in Python
- Fundamental level understanding of Machine Learning
- Note: The above knowledge is must-have for the participants to fully appreciate the training content.
- Knowledge of Deep Neural Network models
- MNIST database
Terms And Conditions
- We will Provide Supporting to resolve Student practical Issues.
- We will provide server Access and 100% Lab Facility.
- Resume Preparation.
- Interview Questions & Answers.
- We will conduct mock interviews. Student also gets 100% supporting before and after getting job.
Deep Learning Online Training Course Content
- Data science & its importance
- Key Elements of Data Science
- Artificial Intelligence & Machine Learning Introduction
- Who uses AI?
- AI for Banking & Finance, Manufacturing, Healthcare, Retail and Supply
- What makes a Machine Learning Expert?
- What to learn to become a Machine Learning Developer?
- Review of Machine Learning: Regression, Classification, Clustering,
- Reinforcement Learning, Underfitting and Overfitting, Optimization
- Deep Learning: A revolution in Artificial Intelligence
- 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
Neural Networks Basics
- How Neural Networks Work?
- Various activation functions – Sigmoid, Relu, Tanh
- Perceptron and Multi-layer Perceptron
- What is TensorFlow?
- TensorFlow code-basics
- Graph Visualization
- Constants, Placeholders, Variables
- Creating a Model
- Step by Step - Use-Case Implementation
- Introduction to Keras
- Understand Neural Networks in Detail
- Illustrate Multi-Layer Perceptron
- Backpropagation – Learning Algorithm
- Understand Backpropagation – Using Neural Network Example
- MLP Digit-Classifier using TensorFlow
Deep Neural Networks
- Why Deep Networks
- Why Deep Networks give better accuracy?
- Understand How Deep Network Works?
- How Backpropagation Works?
- Illustrate Forward pass, Backward pass
- Different variants of Gradient Descent
- Types of Deep Networks
- Batch Normalization
- Activation and Loss functions
- Hyper parameter tuning
- Training challenges and techniques
- Optimizers, learning rate, momentum, etc.
Convolutional Neural Networks
- Introduction to CNNs
- CNNs Application
- Architecture of a CNN
- Forward propagation & Backpropagation for CNNs
- Convolution, Pooling, Padding & its mechanisms
- Understanding and Visualizing a CNN
- An overview of pre-trained models (AlexNet, VGGNet, InceptionNet &
- ResNet) and Transfer Learning
- Image classification using CNN
Advanced Computer Vision
- Auto encoders
- Semantic segmentation
- Siamese Networks
- Object & face recognition using techniques above
RNN And LSTM
- Introduction to Sequential data
- Word embeddings and lang translation
- RNNs and its mechanisms
- Vanishing & Exploding gradients in RNNs
- Time series analysis
- LSTMs with attention mechanism
Visualization Using Tensorboard
- What is Tensor board?
- Test vs Train set accuracy
- Occlusion Experiment
- CAM, Saliency and Activation maps
- Visualizing Kernels
- Style transfer
Reinforcement Learning And Gans
- How GANs work?
- Applications of GANs (Generative adversarial networks)
0.00 average based on 0 ratings