Journal entries of my machine learning journey to share the successes and challleniges along the way
Welcome to another chapter of the machine learning scholar adventure. It’s been a while since the last chapter update I posted but I’m so glad to be back on this exhilarating path! One thing I’ve come to realise is that progress is not linear… Continue reading
What progress did I make? Machine learning Deep Learning for Coders with Fastai and Pytorch: AI Applications Without a PhD The fastai book is still my main focus since my last progress update on MLSA: Chapter 3. Chapter 3: Data ethics I gained further insight into the ethical implications of AI technology. This was my first time where I realised that just like any other technology, data based creations are just as likely to be misused in the same way as other technological developments. Humans make technology generally to improve life but there is no guarantee that something which is… Continue reading
What progress did I make? Machine learning Deep Learning for Coders with Fastai and Pytorch: AI Applications Without a PhD The fastai book has been my main focus since my last progress update on MLSA: Chapter 2. I have also been supplementing working through the book with viewing the related online course videos. I really like the fastai book because the approach is based on top down learning where the emphasis is on using deep learning as soon as possible. This was really powerful because the field’s myths were demystified rapidly, I got to see the capability of models available… Continue reading
What progress did I make? Hell yeeeeaaah ? I’ve made strong positive progress with my MLSA and I am super happy! I can finally feel the strength of the momentum I’ve been aspiring for. I’ve gotten better with prioritising my learning, maintaining consistency and keeping myself enthused. Without further ado, here’s what I’ve been up to: Machine learning Kaggle learn: Pandas This was a great introduction to the capabilities of the pandas library. Whilst I knew very basic pandas e.g. selecting rows, columns etc, I was less knowledgable about intermediate concepts. Now, I’m more comfortable with maps, understand grouping and… Continue reading
What a year! As you may have noticed, I’ve been quiet on updating progress with my MLSA (machine learning scholar adventure). You may be wondering why? Well, unless you’ve been living under a rock…the world has been suffering from the effects of covid-19 for most of this year. Whilst I’m fortunate enough not to have been infected (unless I’m asymtomatic), it did affect other areas of my life negatively. During that time, I was busy putting out multiple fires and focusing on staying sane by looking after my physical, mental and emotional health. I’m glad that I survived with everything… Continue reading
What progress did I make? Wow, can’t believe how quickly time has flown by. With regards to my MLSA, there has been both positive developments and negative occurrences, I’ve learned how to be better from both. One of the major things I’ve realised is how critical it is that I approach this endeavour with maximum integrity! Part of this means being really genuine and honest with the struggles I’ve been facing so that if others ever feel like giving up or they can’t make it or they aren’t good enough to be in this field, they can look at my… Continue reading
What is the adventure? The machine learning scholar adventure (MLSA) is a self-directed learning journey designed to ramp up my knowledge in machine learning rapidly ? It’s inspired by the structure of the OpenAI’s Scholars program, which works by providing a stipend for 4 months for full time study, pairing you with a mentor and also giving cloud credits for machine learning experiments! Even though, I’m not able to pursue it (e.g. one limitation is that only those in the US can apply), I believe that the most important aspects can be replicated! Consequently, I will follow a curriculum, blog regularly (at… Continue reading
