TensorFlow Training Objectives
- Not really, despite the numbers you see, keep in mind the 'Google' crowd alone will be enough to keep TensorFlow alive as far as it's suitability of research. Furthermore, TensorFlow 2.0 may appeal to the research audience with eager mode and native Keras integration..
- A myriad of tools and frameworks run in the background which makes Tesla's futuristic features a great success. One such framework is PyTorch. PyTorch has gained popularity over the past couple of years and it is now powering the fully autonomous objectives of Tesla motors
- PyTorch has long been the preferred deep-learning library for researchers, while TensorFlow is much more widely used in production. PyTorch's ease of use combined with the default eager execution mode for easier debugging predestines it to be used for fast, hacky solutions and smaller-scale models..
- Finally, Tensorflow is much better for production models and scalability. It was built to be production ready. Whereas, PyTorch is easier to learn and lighter to work with, and hence, is relatively better for passion projects and building rapid prototypes.
- It's so complicated in large part because the core APIs have been totally redesigned about 3 different times. Standard TF code today looks totally different than 3 years ago. So it's hard to piece together all the tutorials you find since they're often approach similar things completely differently..
- TensorFlow provides pre-built functions and advanced operations to ease the task of building different neural network models. It provides the required infrastructure and hardware which makes them one of the leading libraries used extensively by researchers and students in the deep learning domain.
- Just start learning it. 2 weeks. after 1 or 2 days, you will be good enough to train your own classifier with CNN, using Regularization techniques. Keras as part of tf 2 is pretty easy and can be learned within a week.
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- Students who all want to become TensorFlow developers
- TensorFlow is an open-source library developed by Google primarily for deep learning applications. It also supports traditional machine learning. TensorFlow was originally developed for large numerical computations without keeping deep learning in mind.
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Top Skills You Will Gain
- R Programming, Python, SAS
- Artificial Intelligence
- Deep Learning
- Machine Learning
- Statistics, Naive Bayes
- Linear Algebra, CART
- Programming, Neural Networks
- Data Mining, Visualization
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TensorFlow Course Curriculam
Trainers Profile
Trainers are certified professionals with 11+ years of experience in their respective domains as well as they are currently working with Top MNCs. As all Trainers from TensorFlow Course are respective domain working professionals so they are having many live projects, trainers will use these projects during training sessions.
Pre-requisites
Syllabus of TensorFlow Online Course Download syllabus
- Deep Learning: A revolution in Artificial Intelligence
- Limitations of Machine Learning
- Discuss the idea behind Deep Learning
- Advantage of Deep Learning over Machine learning
- 3 Reasons to go Deep
- Real-Life use cases of Deep Learning
- Scenarios where Deep Learning is applicable
- Scalars
- Vectors
- Matrices
- Tensors
- Hyperplanes
- Probability
- Conditional Probabilities
- Posterior Probability
- Distributions
- Samples vs Population
- Resampling Methods
- Selection Bias
- Likelihood
- Regression
- Classification
- Clustering
- Reinforcement Learning
- Underfitting and Overfitting
- Optimization
- Convex Optimization
- Defining Neural Networks
- The Biological Neuron
- The Perceptron
- Multi-Layer Feed-Forward Networks
- Training Neural Networks
- Backpropagation Learning
- Gradient Descent
- Stochastic Gradient Descent
- Quasi-Newton Optimization Methods
- Generative vs Discriminative Models
- Linear
- Sigmoid
- Tanh
- Hard Tanh
- Softmax
- Rectified Linear
- Loss Functions
- Loss Function Notation
- Loss Functions for Regression
- Loss Functions for Classification
- Loss Functions for Reconstruction
- Hyperparameters
- Learning Rate
- Regularization
- Momentum
- Sparsity
- Defining Deep Learning
- Defining Deep Networks
- Common Architectural Principals of Deep Networks
- Reinforcement Learning application in Deep Networks
- Parameters
- Layers
- Activation Functions – Sigmoid, Tanh, ReLU
- Loss Functions
- Optimization Algorithms
- Hyperparameters
- Summary
- What is TensorFlow?
- Use of TensorFlow in Deep Learning
- Working of TensorFlow
- How to install Tensorflow
- HelloWorld with TensorFlow
- Running a Machine learning algorithms on TensorFlow
- Introduction to CNNs
- CNNs Application
- Architecture of a CNN
- Convolution and Pooling layers in a CNN
- Understanding and Visualizing a CNN
- Transfer Learning and Fine-tuning Convolutional Neural Networks
- Introduction to RNN Model
- Application use cases of RNN
- Modelling sequences
- Training RNNs with Backpropagation
- Long Short-Term memory (LSTM)
- Recursive Neural Tensor Network Theory
- Recurrent Neural Network Model
- Restricted Boltzmann Machine
- Applications of RBM
- Collaborative Filtering with RBM
- Introduction to Autoencoders
- Autoencoders applications
- Understanding Autoencoders
- Variational Autoencoders
- Deep Belief Network
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Exam & Certification
- Participate and Complete One batch of TensorFlow Training Course
- Successful completion and evaluation of any one of the given projects
- Complete 85% of the TensorFlow Certification course
- Successful completion and evaluation of any one of the given projects
- Oracle Certified Associate (OCA)
- Oracle Certified Professional (OCP)
- Oracle Certified Expert (OCE)
- Oracle Certified Master (OCM)
- Learn About the Certification Paths.
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