What do you do if you want to level up your understanding of neural networks in Machine Learning?

The starting of my journey in neural networks was based on fundamentals, somewhat exploring the basic neural network in the brain. Starting from neurons, basic units, it was only found that these take inputs, multiply the inputs by weights that indicate the importance of these input signals, and use an activation function in order to get an output. For example, understanding forward propagation, whereby information feeds through the network to give a result, added depth to my understanding and set a platform that made many topics less complex and gave a reflection on how neurons in our brain work to process information.

Start with the basics to get a good grasp of any deep learning applications in the future. First, understand the intuitions and the NN elements, and then build a simple one from scratch. My first go-to recommendations are StatQuest YouTube videos (intuition) and the Coursera courses from Andrew Ng. Then, you can study further through other resources like videos, books, and the classic framework implementations.

Concepts like backpropagation, loss functions, and optimization algorithms deepen your understanding of how neural networks learn and improve over time. Exploring different types of networks such as convolutional neural networks (CNNs) for image recognition or recurrent neural networks (RNNs) for sequential data analysis can provide practical insights into their applications. Additionally, understanding regularization techniques like dropout and batch normalization can help prevent overfitting and improve generalization.

Scientists often describe Neural Networks (NNs) as "black boxes" for several reasons. Numerous weights and biases are involved and adjusted, and various hyperparameters - such as the number of nodes, hidden layers, learning rate, optimization algorithm, regularization, dropout, and cost function and many more - require extensive tuning to achieve an optimized model. To truly understand NNs, start by grasping the basic architecture, then examine each hyperparameter and interpret how it functions step by step. Essentially, NNs are an advanced extension of linear and logistic regression, with additional complexities introduced by the cost function. Different NNs : DNN, CNN, RNN, GNN,.....

If you're a chemist, biologist, or life science enthusiast, you might be interested in Graph Neural Networks (GNNs). GNNs treat molecules or chemical structures as graphs, composed of nodes and edges, and automatically extract features from them to learn and make predictions. This architecture has various applications, such as toxicity prediction and property predictions. Interestingly, graph neural networks are also used in everyday life, like how Facebook suggests friends of friends or Netflix recommends movies based on popularity, genres, and more.

Going further into machine learning, I came across specialized architectures: the convolutional neural networks (CNNs) and recurrent neural networks (RNNs). I have applied CNNs towards the task of building a system that very precisely detects faces from real-time video feeds: a tool towards better security. I developed one for RNNs that understand users' queries based on previous interactions, and through that, there was enhanced, improved engagement, and user satisfaction through chatbots in customer service. These projects indeed placed the practical footings of appropriate neural network architecture selection, hinged on data, and applications.

Exploring LSTMs and GRUs enhances your grasp of sequence modeling. These specialized RNN variants, particularly LSTMs, tackle issues like the vanishing gradient problem and long-range dependencies in data. LSTMs, with their memory cell and gating mechanisms, excel at retaining information over time. GRUs, a simpler version, also regulate data flow efficiently. Mastering these models helps tackle complex tasks like language processing and time series analysis more effectively.

To level up your understanding of neural networks in Machine Learning, dive deeper into the intricacies of this powerful technique. Start by studying the fundamentals of neural network architecture, including layers, activation functions, and optimization algorithms. Explore advanced topics such as convolutional neural networks (CNNs) for image processing and recurrent neural networks (RNNs) for sequential data analysis. Experiment with different network architectures and hyperparameters through hands-on projects and coding exercises. Engage with research papers, online courses, and tutorials to stay updated on the latest advancements.

No proper understanding will come without practice! You'll forget about it and... what's the fun of learning something new without using it at all? Data science is a field that allows us to test, and we have plenty of free and low-cost resources to do it! Pick up a dataset and a tutorial, and apply the learned skills. If following a tutorial, after successfully reproducing it, try to incorporate the new skills into a personal project you're more interested in. That's how you learn more effectively and stand out!

For hands-on practice as a beginner , consider using datasets like the MNIST Fashion dataset, which contains images of clothing items. This dataset is ideal for experimenting with neural networks as it allows you to explore image classification tasks. By building models to classify different types of clothing, you can gain insights into network architectures and optimization methods while working with real-world data.

To truly understand neural networks, I recommend starting with hands-on practice by building simple models using TensorFlow or another framework. Begin with the Iris dataset to understand the basics of classification. Progress to the Boston Housing dataset to hone your regression techniques. Then, tackle the Breast Cancer dataset to delve into binary classification, crucial for applications like medical diagnostics. For those ready for advanced challenges, the Amazon Review dataset is excellent for practicing large-scale data handling and sentiment analysis. Each dataset offers unique learning experiences, preparing you for a range of real-world applications.

Good interest in mathematics is required when studying neural networks. One embarks on the level of linear algebra, where various operations are to be implemented, such as matrix multiplication, which forms the basis for data transformation in neural networks. A calculus understanding, specifically going through differential calculus to understand gradient descent as one of the optimization techniques towards effectively minimizing a cost or loss function. Probability and statistics come into play in modeling uncertainty and learning how we can be in a position to assess the performance of our built models, including concepts such as confidence intervals and hypothesis testing. These concepts will help you develop robust neural networks.

Start from joining LinkedIn groups, where you can be attached to both newcomers and world experts. Participate in Kaggle competition opportunities: hands-on practice and real problems. You can also keep up with Medium or Towards Data Science to see recent research and insights from fellow enthusiasts. Keep up with the latest papers on PapersWithCode, and in another couple of weeks, you'll be able to watch top ML research forum live webinars from the likes of DeepLearning.AI. These would, in the process, open up your mind and deepen your understanding when you put them in practice, possibly involving your community by them giving feedback. This comprehensive approach would help you to keep abreast in ML technology and methodologies.

- Explore how the structure and function of the brain inspire neural network architectures, diving into neuroscience alongside machine learning. - Jump into GANs, which create synthetic data. They're used in cool things like generating realistic images or even deepfakes. - Check out CapsNets, a newer alternative to traditional neural networks, particularly great for understanding spatial relationships in images.

Once you learned how the distinct types of NN structure work, try using pre-trained models (e.g., from huggingface or OpenAI) and fine-tune them! That's fun, that's effective, that's powerful!

If you want to level up in neural networks, which are, by the way, some of the most incredible things happening in technology today, you’ve got to dive deep. Really deep. Neural networks, they’re the future. First thing, you’ve got to look at the leaders in the field. Learn from them. Read their papers, watch their lectures. Then, practice, and practice. You can’t just read about it; you have to do it. Build models, experiment with different architectures. Break things and then figure out why they broke. Don’t do this alone. Network. Join groups, talk to experts. Get out there, share your ideas, get feedback. Challenge yourself. Join Kaggle.