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Learn TensorFlow, Deep Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders and Reinforcement Learning From Industry Experts
Have you ever wondered how self-driving cars are running on roads or how Netflix recommends the movies which you may like or how Amazon recommends you products or how Google search gives you such an accurate results or how speech recognition in your smartphone works or how the world champion was beaten at the game of Go?
Machine learning is behind these innovations. In the recent times, it has been proven that machine learning and deep learning approach to solving a problem gives far better accuracy than other approaches. This has led to a Tsunami in the area of Machine Learning.
Most of the domains that were considered specializations are now being merged into Machine Learning. This has happened because of the following:
Every domain of computing such as data analysis, software engineering, and artificial intelligence is going to be impacted by Machine Learning. Therefore, every engineer, researcher, manager or scientist would be expected to know Machine Learning.
So naturally, you are excited about Machine learning and would love to dive into it. This specialization is designed for those who want to gain hands-on experience in solving real-life problems using machine learning and deep learning. After finishing this specialization, you will find creative ways to apply your learnings to your work. For example
See you in the specialization and happy learning!
Deep Learning Applications, Artificial Neural Network, TensorFlow Demo, Deep Learning Frameworks
Installation, Creating Your First Graph and Running It in a Session, Managing Graphs, Lifecycle of a Node Value, Linear Regression with TensorFlow, Implementing Gradient Descent, Feeding Data to the Training Algorithm, Saving and Restoring Models, Visualizing the Graph and Training Curves Using TensorBoard, Name Scopes, Modularity, Sharing Variables
From Biological to Artificial Neurons, Training an MLP with TensorFlow’s High-Level API, Training a DNN Using Plain TensorFlow, Fine-Tuning Neural Network Hyperparameters
Vanishing / Exploding Gradients Problems, Reusing Pretrained Layers, Faster Optimizers, Avoiding Overfitting Through Regularization, Practical Guidelines
The Architecture of the Visual Cortex, Convolutional Layer, Pooling Layer, CNN Architectures
Recurrent Neurons, Basic RNNs in TensorFlow, Training RNNs, Deep RNNs, LSTM Cell, GRU Cell, Natural Language Processing
Efficient Data Representations, Performing PCA with an Undercomplete Linear Autoencoder, Stacked Autoencoders, Unsupervised Pretraining Using Stacked Autoencoders, Denoising Autoencoders, Sparse Autoencoders, Variational Autoencoders
Learning to Optimize Rewards, Policy Search, Introduction to OpenAI Gym, Neural Network Policies, Evaluating Actions: The Credit Assignment Problem, Policy Gradients, Markov Decision Processes, Temporal Difference Learning and Q-Learning, Learning to Play Ms. Pac-Man Using Deep Q-Learning
In this project, you will build a basic neural network to classify if a given image is of cat or not.
Download images of various animals and then download the latest pretrained Inception v3 model. Run the model to classify downloaded images and display the top five predictions for each image, along with the estimated probability.
Build a model to classify clothes into various categories in Fashion MNIST dataset.
This is a time series prediction task: you are given snapshots of polarimetric radar values and asked to predict the hourly rain gauge total.
Our course is exhaustive and the certificate rewarded by us is proof that you have taken a big leap in Machine Learning and Deep Learning.
The knowledge you have gained from working on projects, videos, quizzes, hands-on assessments and case studies gives you a competitive edge.
Highlight your new skills on your resume, LinkedIn, Facebook and Twitter. Tell your friends and colleagues about it.
On completing this course, you will be able to have a complete understanding of how to train a Deep Learning network. Also, as part of the course, you will be working on 4 real-world projects which will give you full expertise on how to build neural networks. The course also enables you to avoid the challenges of overfitting, underfitting, data augmentation etc in real-world scenarios.
You can check https://youtu.be/dXCx4anEcgU for watching the Course Preview.
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