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Live from Apple’s virtual 2020 iPhone event

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Apple’s big iPhone event is finally here – virtual, which is to be expected these day. This is already the second virtual event Apple has hosted this fall, following one in September at which it revealed the Apple Watch Series 6 and a new iPad Air. This time around, we’re going to see what the iPhone 12 looks like, as well as how many colors and sizes it comes in.

There’s also supposed to be plenty of other news, including a new smaller HomePod mini, maybe an updated Apple TV, possibly a number of different headphone products and more. Will we get our first glance at the first shipping ARM-based Mac to use Apple’s in-house processors? Probably not, but maybe!

We’re going to be following along live and offering commentary below, and you can also tune in live to the video stream right here. Everything gets underway at 10 AM PT/ 1 PM ET.

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How I passed the TensorFlow Developer Certification Exam

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Curriculum — what I studied to build the skills necessary for passing the exam

It should be noted that before I started studying for the exam, I had some hands-on experience building several projects with TensorFlow.

The experienced TensorFlow and deep learning practitioner will likely find they can go through the following curriculum at about the same pace I did (three weeks total), maybe faster.

The beginner will want to take as much time as needed. Remember: building any worthwhile skill takes time.

I’ve listed timelines, costs ($USD), and helpfulness level (towards passing the exam) for each resource. The timelines are based on my experience.

If you want to create a curriculum for yourself, I’d recommend something like the following.

Note: For paid resources, affiliate links have been used. This doesn’t change the price of the resource but if you do happen to purchase one, I will receive a portion of the payment: money I use towards creating resources like this.

Time: 1-hour.

Cost: Free.

Helpfulness level: Required.

This should be your first stop. It outlines the topics which will be covered in the exam. Read it and then read it again.

If you’re new to TensorFlow and machine learning, you’ll likely read this and get scared at all the different topics. Don’t worry. The resources below will help you become familiar with them.

Time: 3 weeks (advanced user) to 3 months (beginner).

Cost: $59 per month after a 7-day free trial, financial aid available through application. If you can’t access Coursera, see the equivalent free version on YouTube.

Helpfulness level: 10/10.

This is the most relevant resource to the exam (and getting started with TensorFlow in general). The careful student will notice the TensorFlow Certification handbook and the outline of this specialization are almost identical.

It’s taught by Laurence Moroney and Andrew Ng, two titans of TensorFlow and machine learning and if I had to only choose one resource to prepare for the exam, this would be it.

I appreciated the short video format and focus on hands-on examples as soon as possible. The multiple code notebooks at the end of each section were must-haves for any practical learner.

A tip for the programming exercises: don’t just fill in the code gaps, write the entire thing out yourself.

Time: 3 weeks (reading cover to cover, no exercises) — 3 months (reading cover to cover and doing the exercises).

Cost: Price varies on Amazon but I picked up a hard copy for $55. You can see all the code for free on GitHub.

Helpfulness level: 7/10 (only because some chapters aren’t relevant to the exam).

At 700+ pages, this book covers basically all of machine learning and thus, some topics which aren’t relevant to the exam. But it’s a must-read for anyone interested in setting themselves a solid foundation for a future in machine learning and not just to pass an exam.

If you’re new to machine learning, you’ll probably find this book hard to read (to begin with). Again, not to worry, you’re not in a rush, learning useful skills takes time.

Put it this way, if you want an idea of the quality of the book, I read the first edition during morning commutes to my machine learning engineer job. And I can tell you, more often than not, I’d end up using exactly what I read in the book during the day.

The 2nd edition is no different, except it’s been updated to cover the latest tools and techniques, namely TensorFlow 2.x — what the exam is based on.

If you’re only after relevant chapters to the exam, you’ll want to read:

  • Chapter 10: Introduction to Artificial Neural Networks with Keras
  • Chapter 11: Training Deep Neural Networks
  • Chapter 12: Custom Models and Training with TensorFlow
  • Chapter 13: Loading and Preprocessing Data with TensorFlow
  • Chapter 14: Deep Computer Vision Using Convolutional Neural Networks
  • Chapter 15: Processing Sequences Using RNNs and CNNs
  • Chapter 16: Natural Language Processing with RNNs and Attention

But for the serious student, I’d suggest the whole book and the exercises (maybe not all, but pick and the choose the ones which suit spark your interests most).

Time: 3-hours (I only watched 3 lectures) — 24-hours (1-hour per lecture, plus 1-hour review each).

Cost: Free.

Helpfulness level: 8/10.

World-class deep learning information from a world-class university, oh and did I mention? It’s free.

The first 3 lectures, deep learning (in general), Convolutional Neural Networks (usually used for computer vision), and Recurrent Neural Networks (usually used for text processing) are the most relevant to the exam.

But again, for the eager learner, going through the whole course wouldn’t be a bad idea.

Be sure to check out the labs and code they offer on GitHub, especially the Introduction to TensorFlow one. And again, I can’t stress the importance of writing the code yourself.

Time: 3-hours (depending on how fast your computer is).

Cost: Free.

Helpfulness level: 10/10 (using PyCharm is a requirement).

The exam takes place in PyCharm (a Python development tool). Before the exam, I’d never used PyCharm. And it’s suggested you get at least somewhat familiar with it before you start.

So to familiarize myself with PyCharm, I went through their getting started series on YouTube which was very straightforward, “here’s what this button does.”

But the main tests were making sure TensorFlow 2.x ran without any issues and my computer could run deep neural networks in a respectable time (my MacBook Pro doesn’t have a Nvidia GPU).

To test this, I replicated the following two TensorFlow tutorials on my local machine:

  1. Image Classification with TensorFlow
  2. Text classification with TensorFlow

Both of these worked fine locally, however, as we’ll see below, as soon as I started the exam, I ran into an issue.

Extras

  • deeplearning.ai videos on Coursera/YouTube — The examination is purely code-based (Python code) but if you want to know what’s going on behind the scenes of the code you’re writing (linear algebra, calculus), I’d jump in and out of videos here as you see fit. For example, if you’re not sure what mini-batch gradient descent is, search “deeplearning.ai mini-batch gradient descent.”
  • TensorFlow documentation — If you’re going to be a TensorFlow practitioner, you’re going to need to be able to read the documentation. If you don’t understand something, write the code and comment it yourself.
  • Coding with TensorFlow on YouTube (playlist) — Most of the TensorFlow in Practice Coursera Specialization in video series on YouTube, taught by the same instructor as well.

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Huawei reports slowing growth as its operations “face significant challenges”

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Huawei announced earnings results today showing that its growth has slowed significantly this year as the Chinese telecom equipment and smartphone giant said its “production and operations face significant challenges.”

While Huawei did not specify trade restrictions in its brief announcement, the company has been hit with a series of commercial trade restrictions by the U.S. government. The full impact of those policies haven’t been realized yet, because U.S. government has granted Huawei several waivers, including one that will delay the implementation of a ban on commercial trade with Huawei and ZTE until May 2021.

During the first three quarters of 2020, the Chinese telecoms and smartphone giant reported revenue of 671.3 billion yuan (about USD $100.7 billion), an increase of 9.9% year-over-year, with a profit margin of 8%. The company said those results “basically met expectations,” but it represents a huge drop from its performance during the same period last year, when Huawei reported 24.4% growth with a profit margin of 8.7%.

Huawei is a privately-held company and its announcement did not break down its results in terms of smartphone or telecoms equipment sales, or other detail.

The company wrote that “as the world grapples with COVID-19, Huawei’s global supply chain is being put under pressure and its production and operations face significant challenges. The company continues to do its best to find solutions, survive and forge forward, and fulfill its obligations to customers and suppliers.”

Other U.S. restrictions include one that would ban Huawei from using U.S. software and hardware in certain semiconductor processes, forcing it to find other sources for chips.

In addition to the U.S., Huawei is also facing scrutiny by other countries, including the United Kingdom, which is planning to implement a new poicy that will bar telecoms from buying new 5G equipment from Huawei to ZTE and require them to remove any parts from those companies that’s already been installed in UK 5G networks by 2027.

Replacing Huawei equipment also presents costly challenges for telecoms, because Huawei is one of the biggest suppliers in the world. Last month, the U.S. Federal Communications Commission said it would cost $1.837 billion to replace Huawei and ZTE networking equipment, with rural telecom networks facing the most financial pressure.

But 2020 has had a few bright spots for Huawei. In July, a report from Canalys found that Huawei overtook Samsung as the leader in global smartphone shipments during the second quarter of 2020, a major milestone because it marked the the first time in nine years that Apple and Samsung didn’t take the top spot on Canalys’ charts. This was partly because smartphone shipments in general have been hurt during the COVID-19 pandemic, but Huawei was helped by sales within China, its domestic market.

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Nordic challenger bank Lunar raises €40M Series C, plans to enter the ‘buy now, pay later’ space

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Lunar, the Nordic challenger bank that started out life as a personal finance manager app (PFM) but acquired a full banking license in 2019, has raised €40 million in Series C funding from existing investors.

The injection of capital follows a €20 million Series B disclosed in April this year and comes on the back of Lunar rolling out Pro paid-for subscriptions — similar to a number of other challenger banks in Europe — personal consumer loans, and the launch of business bank accounts in August.

The latter appears to have been an instant success, perhaps proof there is — like in the U.K. — pent up demand for more accessible banking for sole traders. Just months since launching in Denmark, Lunar Business claims to have signed up more than 50% of all newly founded sole trader businesses in the country.

I’m also told that Lunar has seen “best-in-class” user engagement with users spending €1,100 per month versus what the bank says is a €212 EU average for card transactions. Overall, the bank has 5,000 business users and 200,000 private users across Denmark, Sweden and Norway.

Meanwhile — and most noteworthy — after launching its first consumer lending products on its own balance sheet, Lunar has set its sights on the “buy now, pay later” market, therefore theoretically encroaching on $10.65 billion valued Klarna, and Affirm in the U.S. which just filed to go public. Other giants in the BNPL space also include PayPal.

Lunar founder and CEO Ken Villum Klausen says the “schizophrenic” Nordic banking market is the reason why the challenger is launching BNPL. “It’s the most profitable banking landscape in the world, but also the most defensive, with least competition from the outside,” he says. “This means that the traditional banking customer is buying all their financial products from their bank”.

It is within this context that Lunar’s BNPL products are built as “post-purchase,” where Lunar will prompt its users after they have bought something (not dissimilar to Curve’s planned credit offering). For example, if you were to buy a new television, the app will ask if you want to split the purchase into instalments. “This does not require merchant agreements etc, and will work on all transactions both retail and e-commerce,” explains Klausen.

“We do not view Klarna as a direct competitor as they are not in the Nordic clearing system,” he adds. “Hence, you cannot pay your bills, get your salary and use it for daily banking. Klarna is enormous in Sweden, but relatively small in Denmark, Norway and Finland”.

In total, Lunar has raised €104 million from investors including Seed Capital, Greyhound Capital, Socii Capital and Chr. Augustinus Fabrikker. The challenger has offices in Aarhus, Copenhagen, Stockholm and Oslo, with a headcount of more than 180 employees. It plans to launch its banking app in Finland in the first half of 2021.

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