Get yourself trained on Deep Learning with with this Online Training Deep Learning with Python – A Complete Guide!: 2-in-1.
Online Training Deep Learning with Python – A Complete Guide!: 2-in-1
Deep learning is the next step to machine learning with a more advanced implementation. Deep learning is currently one of the best providers of solutions regarding problems in image recognition, speech recognition, object recognition, and natural language with its increasing number of libraries that are available in Python.With deep learning being used by many data scientists, deeper neural networks are evaluated for accurate results. Deep Learning is revolutionizing a wide range of industries. For many applications, Deep Learning has been proven to outperform humans by making faster and more accurate predictions.This comprehensive 2-in-1 course takes a solution-based approach where every topic is explicated with the help of a real-world example. It is is a unique blend of independent solutions arranged in the most logical manner. Use Python frameworks such as TensorFlow, Caffe, Keras, and Theano for Natural Language Processing, Computer Vision, and more!By the end of the course, youll not only dive into the future of Data Science but also implement intelligent systems using Deep Learning with Python!Contents and OverviewThis training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Deep Learning with Python, covers implementing intelligent systems using deep learning with Python. This course takes you from basic calculus knowledge to understanding backpropagation and its application for training in neural networks for deep learning and understand automatic differentiation. Through the course, we will cover thorough training in convolutional, recurrent neural networks and build up the theory that focuses on supervised learning and integrate into your product offerings such as search, image recognition, and object processing. Also, we will examine the performance of the sentimental analysis model and will conclude with the introduction of Tensorflow. By the end of this course, you can start working with deep learning right away. This course will make you confident about its implementation in your current work as well as further research.The second course, Python Deep Learning Solutions, covers over 20 practical videos on neural network modeling, reinforcement learning, and transfer learning using Python. This course provides a top-down and bottom-up approach to demonstrating Deep Learning solutions to real-world problems in different areas. These applications include Computer Vision, Generative Adversarial Networks, and time series. This course presents technical solutions to the issues presented, along with a detailed explanation of the solutions. Furthermore, it provides a discussion on the corresponding pros and cons of implementing the proposed solution using a popular framework such as TensorFlow, PyTorch, and Keras. The course includes solutions that are related to the basic concepts of neural networks; all techniques, as well as classical network topologies, are covered. The main purpose of this video course is to provide Python programmers with a detailed list of solutions so they can apply Deep Learning to common and not-so-common scenarios.By the end of the course, youll dive into the future of Data Science and implement intelligent systems using Deep Learning with Python.About the AuthorsEder Santana is a PhD candidate on Electrical and Computer Engineering. His thesis topic is on Deep and Recurrent neural networks. After working for 3 years with Kernel Machines (SVMs, Information Theoretic Learning, and so on), Eder moved to the field of deep learning 2.5 years ago, when he started learning Theano, Caffe, and other machine learning frameworks. Now, Eder contributes to Keras: Deep Learning Library for Python. Besides deep learning, he also likes data visualization and teaching machine learning, either on online forums or as a teacher assistant.Indra den Bakker is an experienced Deep Learning engineer and mentor. He is the founder of 23insights (part of NVIDIA’s Inception program), a machine learning start-up building solutions that transform the world’s most important industries. For Udacity, he mentors students pursuing a Nanodegree in Deep Learning and related fields, and he is also responsible for reviewing student projects. Indra has a background in computational intelligence and worked for several years as a data scientist for IPG Mediabrands and Screen6 before founding 23insights.
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As a society, we spend hundreds of billions of dollars measuring the return on our financial assets. Yet, at the same time, we still haven’t found convincing ways of measuring the return on our investments in developing people.
And I get it: If my bank account pays me 1% a year, I can measure it to the penny. We’ve been collectively trained to expect neat and precise ROI calculations on everything, so when it’s applied to something as seemingly squishy as how effectively people are learning in the workplace, the natural inclination is to throw up our hands and say it can’t be done. But we need to figure this out. In a world where skills beat capital, the winners and losers of the next 30 years will be determined by their ability to attract and develop great talent.
Fortunately, corporate learning & development (L&D), like most business functions, is evolving quickly. We can embrace some level of ambiguity and have rigor when measuring the ROI of learning. It just might look a little different than an M.B.A. would expect to see in an Excel model.