Step 4 External Work Experience: Fill & Download for Free

23 year old, so this imply you have an experience of 1 year plus few months after your graduation in best case.I had also joined TCS post my graduation and have been through various appraisal cycles.First of all, I would like to tell you do not think to switch company without an experience of at least 2–3 years. This is because if you have an experience less than that you will be considered as a fresher. So try to complete 2 years in TCS. In meanwhile, try to figure out what you want to do post 5 year. Do you want to be in IT Industry?Path 1:If the answer is NO, then try to figure out best option for yourself in further studies like MBA, M Tech, Banking Exams etc.If you are preparing for MBA, then make sure you work hell hard. Try to get more than 97 percentile and get into some A grade college.But just wait, you want money, but even if you clear MBA then first you will have to pay a hefty fee of 16–20 lakh rupees. Considering you take a education loan for the same at 10 % intrest then it sums upto be 25–30 lakh rupees. Also you need 2.5 lakh rupees for two year to manage you expense. Over and above that you will lose two year of salary which you would have earned if you were working.All this will work out only when you get placed for 25–30 lakh of package or above.If you are willing to do MBA for knowledge and career stream perspective then you can ignore the above PnL(profit and loss). Same would be the case for MS.Path 2If the answer is YES, then try to find the hot selling technology and learn them. Few such technologies are Blockchain, Big Data, data analytics and intelligence, DevOps etc. Start studying about their pros and cons. The ones listed above are there to stay for couple of decades.Once you are done and shortlisted one such technology then start learning about them by online videos. You can attend the online courses available in iEvolve. Attain E1, E2 level of competency and then try to do an external certification sponsored from TCS.Post certification meet your delivery manager directly and talk to him regarding your aspirations and tell him that you want to move in whatever technology you have gained competency. 70% chances is that he will ignore whatever you said the minute you leave his cabin. In such case report to your RMG or HR. When they reach your delivery manager then he would need to take action on your aspirations. Hopefully you will be allocated to a team working in the technology you wanted to. Work in that for one year or couple of projects. Gain the work experience. Post that float your resume on Naukri, LinkedIn etc.This process will take about 2 years of time. But the time will be worth it. Also I recommend you to join company like Accenture or Deloitte. These companies will give you about 50% hike on your CTC at that time. Your CTC after 4 year of experience will be around 5lpa. So you will getting an offer of 7.8 to 8 lpa as fixed along with a Joining bonus of 1 lakh ( in case you join early) and upto 15% of bonus on CTC along with Hotskill bonus.In this whole process of learning and changing domain you may have to take a hit on your ratings but do take it because it's worth it.Path 3You need to follow half of the process in the path 2, till taking external certification.After that work on some freelancing project. Get some work experience. Tweak your resume and apply in other companies.I personally will not suggest this option as shortcuts will harm you in long term gain.Path 4Ask onsite from your delivery manager and hope you get it ASAP . But I may warn you, getting onsite is getting tougher day by day. This is the carrot provided by managers to hamster like you and me.Chose your path wisely and all the best.Edit 1:We as Indian fear a lot, the fear is inculcated to us since childhood. We are taught to be content with what we have in life.The same is observed in our professional life.We fear to take bold step, we fear to ask question.In Indian IT circuit what I find amusing is at time of interview we are asked where do you see yourself in next five years.Have you ever asked the same from your management? Even if you ask they will have no answer, because they are also unaware about the answer for themselves.Do not change your job just for some 40–50 % but do it for your holistic career growth and happiness.Belive me, if you are good in what you do, you have lot of opportunities in market.Don't get stuck in the vicious circle of yearly appraisal, half yearly and yearly ratings, promotion opportunity and distant dream of onsite. This will be a never ending loop.But try to make linear goals. Have you deadlines and work accordingly. And be honest with yourself.Don't be just a hampster

When external drive unexpectedly disconnect and a warning message appears a “Disk ejected improperly” or “ the disk you inserted was not readable by the computer”. It might happens in the middle of a file transfer, the drive view disappears and it halts the transfer process.Usually, this problem mostly happens due to hardware-based issues such as power loss to the device or cable is faulty. As a result, the first step is to tackle the problem related to power cable, connector etc.Here are possible causes of external drive keep disconnecting:The hard drive is not plugged properly, connect it properlyData cable is damaged try new cableUSB cable connector is damagedA USB port( Thunderbolt port) is damaged, it might be a hardware problem of your Mac or Windows, so try a different port.System is very slow and taking more time to initializeTest your external hard drive on different systemDespite these approaches, If you happen to find a drive will continue to eject, when the same drive is attached to another Mac or to a PC. Try these experimented solutions that work for such types of problems. (I am explaining Mac solutions)You can utilize the Disk utility function, a built-in Mac tool that can repair your external hard drive. It can fix numerous hard drive problems such as external hard drives to keep disconnecting issues on your computer or laptop.Here is an instruction on how you can repair your external hard drive by using Disk utility.Step 1: Click on the initialize button when the error message dialogue appears. After that Disk utility window will open.Step 2: Click on the view option in the menu bar, and choose Show all devices.Step 3: On the left pane of the display screen, click on the external device and then click on the First Aid tab option.Step 4: Click on the Repair Disk at the bottom window for a start repairing process.Step 5: This step will take several minutes for processing. Once the repair is done. Connect the drive and check if the problem is fixed.

If you have coding experience with an engineering background or relevant knowledge in mathematics and computer science, in just two months you can become proficient in deep learning. Hard to believe? Here's a four-step process that makes it possible.For more inspiration check out the following video by Andrew NgStep 1: Learn Machine Learning Basics (Optional, but highly recommended)Start with Andrew Ng's Class on machine learning Machine Learning - Stanford University | Coursera. His course provides an introduction to the various Machine Learning algorithms currently out there and, more importantly, the general procedures and methods for machine learning, including data preprocessing, hyper-parameter tuning, and more.I would also recommend reading the NIPS 2015 Deep Learning Tutorial by Geoff Hinton, Yoshua Bengio, and Yann LeCun, which offers an introduction at a slightly lower level.Step 2: Dig into Deep LearningMy personal learning preference is to watch lecture videos, and there are several excellent courses online. Here are few classes I especially like and can recommend:Deep learning at Oxford 2015 Taught by Nando de Freitas who expertly explains the basics, without overcomplicating it. Start with Lectures 9 if you are already familiar with Neural Networks and want to go deep. He uses Torch framework in his examples. (Videos on Youtube)Neural Networks for Machine Learning: Geoffrey Hinton’s class on Coursera. Hinton is an excellent researcher who demonstrated the use of generalized backpropagation algorithm and was crucial to the development of deep learning. I have utmost respect for him, but I found the delivery of this course bit unorganized. Furthermore, coursera messes up with the placement of quizzes.Neural Networks Class by Hugo Larochelle: Another excellent courseYaser Abu-Mostafa’s machine learning course: More theory if you are interested.If you are more into books, here are some excellent resources. Go ahead and check them out, I won’t judge.Neural Networks and Deep Learning Book by Michael Nielsen’s: Online book and has several interactive JavaScript elements to play with.Deep Learning Book by Ian Goodfellow, Yoshua Bengio and Aaron Courville: Bit denser but never the less a great resourceStep 3: Pick a focus area and go deeperIdentify what you are passionate about and go deeper. The field is vast, so this list is in no way a comprehensive list.Computer vision : Deep learning has transformed this area. Stanford’s CS231N course by Andrej Karpathy’s course is the best course I have come across; CS231n Convolutional Neural Networks for Visual Recognition. It teaches you the basics and up to covnets, as well as helping you to set up GPU instance in AWS. Also, check out Getting Started in Computer Vision by Mostafa S. IbrahimNatural Language Processing (NLP) : Used for machine translation, question and answering, sentiment analysis. To master this field, an in-depth understanding of both algorithms and the underlying computational properties of natural languages is needed. CS 224N / Ling 284 by Christopher Manning is a great course to get started. CS224d: Deep Learning for Natural Language Processing, another Stanford class by David Socher (founder of MetaMind)is also an excellent course which goes over all the latest Deep learning research related to NLP. For more details see How do I learn Natural Language Processing?Memory Network (RNN-LSTM) : Recent work in combining attention mechanism in LSTM Recurrent Neural networks with external writable memory has meant some interesting work in building systems that can understand, store and retrieve information in a question & answering style. This research area got its start in Dr. Yann Lecun’s Facebook AI lab at NYU. The original paper is on arxiv: Memory Networks. There’re many research variants, datasets, benchmarks, etc that have stemmed from this work, for example, Metamind’s Dynamic Memory Networks for Natural Language ProcessingDeep Reinforcement Learning : Made famous by AlphaGo, the Go-playing system that recently defeated the strongest Go players in history. David Silver’s (Google Deepmind) Video Lectures on RL and Professor Rich Stutton’s Book is a great place to start. For a gentle introduction to LSTM see Christopher’s post on Understanding LSTM networks & Andrej Karpathy’s The Unreasonable Effectiveness of Recurrent Neural NetworksGenerative Models: While discriminatory models try to detect, identify and separate things, they end up looking for features which differentiate and do not understand data at a fundamental level. Apart from the short-term applications, generative models provide the potential to automatically learn natural features; categories or dimensions or something else entirely. Out of the three commonly used generative models — Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs) and Autoregressive models (such as PixelRNN) , GAN’s are most popular. To dig deeper readOriginal GAN paper by Ian Goodfellow et al.The Laplacian Adversarial Networks (LAPGAN) Paper (LAPGAN) which fixed the stability issueThe Deep Convolutional Generative Adversarial Networks (DCGAN) paper and DCGAN Code which can be used to learn a hierarchy of features without any supervision. Also, check out DCGAN used for Image SuperresolutionStep 4: Build SomethingDoing is key to becoming an expert. Try to build something which interests you and matches your skill level. Here are a few suggestions to get you thinking:As is tradition, start with classifying the MNIST datasetTry face detection and classification on ImageNet. If you are up to it, do the ImageNet Challenge 2016.Do a Twitter sentiment analysis using RNNs or CNNsTeach neural networks to reproduce the artistic style of famous painters (A Neural Algorithm of Artistic Style)Compose Music With Recurrent Neural NetworksPlay ping-pong using Deep Reinforcement LearningUse Neural Networks to Rate a selfieAutomatically color Black & White pictures using Deep LearningFor more inspiration, take a look at CS231n Winter 2016 & Winter 2015 projects. Also keep an eye on the Kaggle and HackerRank competitions for fun stuff and the opportunities to compete and learn.Additional ResourcesHere are some pointers to help you with continuous learningRead some excellent blogs. Both Christopher Olah’s blog & Andrew Karpathy’s Blog do a great job of explaining basic concepts and recent breakthrusFollow influencers on Twitter. Here are a few to get started @drfeifei, @ylecun, @karpathy, @AndrewYNg, @Kdnuggets, @OpenAI, @googleresearch. (see: Who to follow on Twitter for machine learning information ? )Google+ Deep Learning Community Page, by Yann Lecunn, is a good way to keeping in touch with innovations in deep learning as well as communicating with other deep learning professionals and enthusiasts.See ChristosChristofidis/awesome-deep-learning, a curated list of awesome Deep Learning tutorials, projects and communities for more fun.