TensorFlow essentials - TensorFlow Tutorial

- [Instructor] In this session, we are going to explore how you can become a master chef in the world of neural networks using TensorFlow. Think of Keras as your sauce chef, making things easier. But we are also going to dive into the kitchen ourselves and get our hands dirty with TensorFlow's lower-level API for more control and customization. Why use TensorFlow's lower-level API? Think of Keras as your sauce chef, handling the basics so you can focus on creating great recipes or deep learning models. Keras simplifies model building, but sometimes you need more control, like adding a secret ingredient or adjusting the cooking time. This API allows you to write custom loss functions, metrics, initializers, and more. So think of it as like having the freedom to tweak every aspect of your dish to perfection. So let's go over and do a quick tour of TensorFlow. So think of TensorFlow as like a high-powered kitchen appliance, versatile and efficient for numerical computations and machine learning tasks. It's developed by Google's Brain Team. It's the secret sauce behind many of Google's services, like Google Photos and search. So TensorFlow has become a staple in the deep learning community, much like a go-to cookbook in a chef's kitchen. So what are some of TensorFlow's futures? First of all, similarity to NumPy. If you have used the Python library NumPy, TensorFlow will feel familiar, but with the added power of GPU support. So keep in mind at the time of this recording, codespaces do not support GPU. So for the sake of this class, we won't be utilizing GPU, but just know that if you are utilizing TensorFlow in your local environment, and if you do have access to a GPU support, TensorFlow is very similar to NumPy with added GPU support. So imagine NumPy as your basic kitchen tools and TensorFlow as those same tools with turbo boosts. Let's talk about distributed computing. It's like having multiple cooks in the kitchen, each handling different tasks simultaneously across multiple devices. This ensures your complex meal or your deep learning model gets done faster and more efficiently. So let's look at just-in-time compiler next. This feature optimizes your computations for speed and efficiency like a turbocharged blender that prepares everything in no time. So next, let's look at computation graphs. These graphs allow you to export and run your computations in different environments from Python to Java on Android. It's like having a recipe that works perfectly, whether you are cooking at home or in a professional kitchen. Next, reverse mode differentiation. It's a fancy word, but this helps in optimizing your recipes, in other words, models by efficiently computing gradients. Think of it as having a culinary GPS that guides you through the exact steps needed for the perfect dish. So let's look at TensorFlow Python API. First of all, it includes high-level deep learning APIs. Tf.keras makes model building very straightforward and intuitive. It's like a ready-made mix where you just add water and that's it, you have a cake. Next is low level deep learning API. Tf.nn and tf.GradientType give you the fine control needed for custom operations. It's like having access to individual spices and herbs to create your own unique blend. Next, let's look at mathematical operations. Libraries like tf.math, tf.linalgebra, which is linalg, and tf.signal are very essential tools for your mathematical needs. So think of them like your measuring spoons, mixers, and whisks for precise cooking. Next is visualization. Tf.summary integrates with TensorBoard for visualizing your models and their performance. This is like presenting your dish on a beautiful platter to impress your guests. Next up is TensorFlow architecture. At its core, TensorFlow's execution engine ensures efficient operations across multiple devices like a well-coordinated kitchen staff. It can handle tasks on CPUs, GPUs, and even TPUs for faster performance. It supports various languages, including Python, C++, Java, and Swift, making it a versatile tool in your toolkit. This is like having a multi-language cookbook that works anywhere and everywhere. Next, TensorFlow Lite allows you to run models on mobile devices, taking your culinary creations on the go, like a food truck bringing your gourmet meals to the streets. So to conclude, TensorFlow's high level API, Keras, is like your trusty sauce chef. It's great for quick and easy meals, but when you need to prepare a gourmet feast, TensorFlow's lower-level API gives you the control to customize every aspect of your model. This allows you to fine-tune your creations and optimize them for specific tasks. The distributed computing feature is like having a team of sauce chefs in the kitchen, each working on different parts of the meal to ensure everything is perfectly timed and cooked. The just-in-time compiler optimizes your computations, ensuring your models run efficiently and quickly. So with TensorFlow's flexible architecture, you can export and run your computation graphs in different environments, ensuring consistency and performance no matter where you are. This makes TensorFlow a powerful and versatile tool in the world of machine learning.