Learn TensorFlow, 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.
In Self-paced learning, you will get,
In Instructor-led training, you will get
This course is for engineers, product managers and anyone who has a basic know-how of any programming language. We will cover foundations of linear algebra, calculus and statistical inference where ever required so that you can learn the concepts effectively.
It will take around 7 weeks with 6-8 hours of effort per week.
We understand that you might need course material for a longer duration to make most out of your subscription. You will get lifetime access (Till the company is operational) to the course material so that you can refer to the course material anytime.
In online instructor-led training, Sandeep Giri along with his team of experts will train you with a group of our course learners for 45+ hours over online conferencing software like Zoom.
We offer mentoring sessions to our learners with industry leaders and professionals so you can get 1 on 1 help with any questions you may have, whether your questions are technical, job-related or anything else.
It is a paid service and exclusively available to learners enrolling for the course. We will provide more information on subscription information for the same after the course is launched.
At the end, of course, you will work on a real-time project. You will receive a problem statement along with a data-set to work on CloudxLab. Once you are done with the project (it will be reviewed by an expert), you will be awarded a certificate which you can share on LinkedIn.
Enrollment into self-paced course entails 90 days of free access to CloudxLab. Enrollment into instructor-led course entails 90 days of free access to Cloudxlab, depending on date of enrollment.
Course requires a good internet (1 Mbps or more) and a browser to watch videos and do hands-on the lab. We've configured all the tools in the lab so that you can focus on learning and practicing in a real-world cluster.
At CloudxLab, we have always believed in quality education must be affordable for everyone so that we can help learners achieving career goals and build innovative products.
Please follow this post for more details on the financial aid.
Have more questions? Please contact us at firstname.lastname@example.org