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The Python Bible™ | Everything You Need to Program in Python "If you can take just one Python course, make sure it's this one." - A. Barbosa "I have tried many python courses on Udemy but this one is the best of them all." - Natalie "I have other python courses on Udemy but this one is the best by far. It explains things in a very hands on and easy to follow method that will have you understanding what you are doing in no time." - Robert Rodono "High Energy, Interesting and has a sense of humor making the material more comprehensible and clear." - Ronald Cosentino "It is more like a friend showing how to use a new toy than a professor reminding you of your ignorance." - Sylvester Pierce “I was highly impressed with the teaching. “The sessions have always been very engaging and structured in an effective manner. “Ziyad is and will always continue to be an excellent tutor.” - Jessie It's no secret that Project-based learning is proven to be the single most-effective way to learn any skill, but this is especially true for Programming! Hi, I'm Ziyad.

Learn Python - Free Interactive Python Tutorial MIT 6.S094: Deep Learning for Self-Driving Cars 6 Weeks Training in Gurgaon SSDN Technologies is one of the leading training center, offering the 6 weeks summer training opportunity for B.Tech & MCA Students with Live Project Summer training and internship in Gurgaon & Delhi. Utilize the summer vacations and move your steps to the SSDN door, as this is the perfect 6 weeks summer training institute you are looking for. This is the perfect institute where experienced staff will trained you with live projects. Summer training plays a crucial role in the career of graduate or under graduate students. During the 6 weeks training program, students will also get trained with special sessions for “express yourself” personality to achieve the greatness from knowledge and appearance which will be the key to crack the toughest and rigorous interviews. Training Programs Available Here’s the list of technologies for 6 weeks training programs for live projects and Summer training during the summer break.

Cooperative Learning: 7 Free PDF Assessment Instruments Introduction Evaluating cooperative learning activities may seem like a Herculean task. But, actually it's not. Like any other assessment, you must determine in advance what you would like to assess and to what degree. Do you want to evaluate individual success, group success or perhaps, cooperative skills? Actually, I think you may find that it's useful to evaluate all three. I'm relatively sure what you may be thinking here. We teachers already have full plates--with all of the paperwork, lesson planning, endless paper grading, and the myriad of other things that are demanded of us, we just don't need something else to do. However, as you know from your own experiences, kids EXPECT to be evaluated. Pity the poor teacher who would respond to that question with, "No, but this will make great practice." The kids, of course, would immediately shut down. What follows on this page is a small collection of assessment tools that can be used for cooperative learning. Quick Links for THIS Page

Complete Python Masterclass Tim's mission is simple: to make a difference in your life by helping you to become a software developer. Tim does that through his Android, Python and Java courses. When Tim started programming over thirty years ago, there was no online video training. There was no "easy" way to learn. The Internet, in its current form, didn't exist, and as a result, Tim couldn't "Google" for help, or watch videos on Youtube. After learning the hard way, Tim was determined to become the best teacher he could, and to make his training as painless as possible, so that you, or any one else with the desire to become a software developer, could become one. In between doing that, Tim spent the best part of those years as a professional software developer. Tim is relatively unique, in that he is a professional, experienced software developer who also has exceptional teaching skills. Many instructors have no work experience in the field! What does all of this mean for you? Are you ready to start?

Interactive Dashboards and Data Apps with Plotly and Dash So far, we have discussed the basic structure of a Dash app and gone through a brief overview of its main elements: the imports, app instantiation, app layout, callbacks (which we will cover in the next chapter), and running the app. We created a bare-bones app, and then we learned how to add a few HTML elements to it. We are now ready to take our app to the next level—from a layout perspective. We will keep working with the app.layout attribute and control it in a more powerful and flexible way using the Dash Bootstrap Components package. Bootstrap is basically a set of tools that abstract away many details for handling the layout of web pages. Themes: As we will see in a moment, changing the app's theme is as simple as providing an additional argument while instantiating the app. Let's explore these features one by one. Themes First, let's see how easy it is to change the theme of an app. Running the app again, you should see that the theme has changed. Grid system and responsiveness Tip

Deep Learning Course ⇢ François Fleuret You can find here the materials for the EPFL course EE-559 “Deep Learning”. These documents are under heavy development, in particular due to pytorch updates. Please avoid to distribute the pdf files, and share the URL of this page instead. Info sheet: dlc-info-sheet.pdf We will use the pytorch framework for implementations. Thanks to Adam Paszke, Alexandre Nanchen, Xavier Glorot, Matus Telgarsky, and Diederik Kingma, for their help, comments, or remarks. Course material You will find here the slides I use to teach, which are full of “animations” and not convenient to print or use as notes, and the handouts, with two slides per pages. Practical session prologue Helper python prologue for the practical sessions: dlc_practical_prologue.py Lecture 1 (Feb 21, 2018) – Introduction and tensors Lecture 2 (Feb 28, 2018) – Machine learning fundamentals Empirical risk minimization, capacity, bias-variance dilemma, polynomial regression, k-means and PCA. Cross-entropy, L1 and L2 penalty.

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