Neural Networks with TensorFlow and PyTorch Virtual Training Program
Batch Start Date: Next Week
Unleash the power of TensorFlow and PyTorch to build and train Neural Networks effectively
What you’ll learn
- Get hands-on and understand Neural Networks with TensorFlow and PyTorch
- Develop an autonomous agent in an Atari environment with OpenAI Gym
- Develop a multilayer perceptron neural network to predict fraud and hospital patient readmission
- Learn how to build a recurrent neural network to forecast time series and stock market data
- Get familiar with PyTorch fundamentals and code a deep neural network
- Understand how and when to apply autoencoders
- Apply NLP and sentiment analysis to your data
- Build convolutional neural network classifier to automatically identify a photograph
- Know how to build Long Short Term Memory Model (LSTM) model to classify movie reviews as positive or negative using Natural Language Processing (NLP)
- Perform image captioning and grammar parsing using Natural Language Processing
Requirements
Basic knowledge of Python is required. Familiarity with TensorFlow and PyTorch will be beneficial.
Description
TensorFlow is quickly becoming the technology of choice for deep learning and machine learning, because of its ease to develop powerful neural networks and intelligent machine learning applications. Like TensorFlow, PyTorch has a clean and simple API, which makes building neural networks faster and easier. It’s also modular, and that makes debugging your code a breeze. If you’re someone who wants to get hands-on with Deep Learning by building and training Neural Networks, then go for this course.
This course takes a step-by-step approach where every topic is explicated with the help of a real-world examples. You will begin with learning some of the Deep Learning algorithms with TensorFlow such as Convolutional Neural Networks and Deep Reinforcement Learning algorithms such as Deep Q Networks and Asynchronous Advantage Actor-Critic. You will then explore Deep Reinforcement Learning algorithms in-depth with real-world datasets to get a hands-on understanding of neural network programming and Autoencoder applications. You will also predict business decisions with NLP wherein you will learn how to program a machine to identify a human face, predict stock market prices, and process text as part of Natural Language Processing (NLP). Next, you will explore the imperative side of PyTorch for dynamic neural network programming. Finally, you will build two mini-projects, first focusing on applying dynamic neural networks to image recognition and second NLP-oriented problems (grammar parsing).
By the end of this course, you will have a complete understanding of the essential ML libraries TensorFlow and PyTorch for developing and training neural networks of varying complexities, without any hassle.
PyTorch
Based on the Torch library, PyTorch is an open-source machine learning library. PyTorch is imperative, which means computations run immediately, means user need not wait to write the full code before checking if it works or not. We can efficiently run a part of the code and inspect it in real-time. The library is python based built for providing flexibility as a deep learning development platform. Enlisted below are the features that justify PyTorch as a deep learning model:
- Easy to use API
- Python support – PyTorch smoothly integrates with the python data science stack. It is similar to numpy, so if you are already using that, you will feel right at home.
- Dynamic computation graphs – PyTorch provides a framework for us to build computational graphs as we go, and even change them during runtime instead of predefined graphs with specific functionalities. This service is valuable for situations where we don’t know the requirement of memory for creating a neural network.
Other key strengths of the machine learning framework include:
- TorchScript: Provides a seamless transition between graph mode and eager mode to accelerate the path to production.
- Distributed Training: Distributed backend. Torch enables performance optimization in research and production and scalable distributed training.
- Tools and Libraries: A vibrant ecosystem of tools and libraries extends PyTorch and supports development in computer vision, NLP, and more.
TensorFlow
Google’s TensorFlow is a famous open-source deep-learning library for dataflow and differentiable programming across a range of tasks. It is also a symbolic math library and machine learning applications such as neural networks also use this library. Research and production are the primary uses of the library. Enlisted are the features of TensorFlow:
- Secure Model Building: Using intuitive high-level APIs such as Keras, the library allows us to build and train ML models, which makes for quick model iteration and easy debugging.
- ML Production Anywhere: Trains and deploys models in the cloud, on-prem, in the browser, or on-device irrespective of the language the user uses.
- Robust Experimentation for Research: A flexible and straightforward architecture to take new ideas from concept to code, to state-of-the-art models, and publication faster
Components of TensorFlow:
- Tensor
Tensor is the core framework of the library that is responsible for all computations in TensorFlow. A tensor is a vector or matrix of n-dimensions that represents all types of data. Values have identical data types with a known shape in a Tensor. The dimensionality of the matrix or array is the shape of the data. An input data or the result of computation generally originates a tensor. A graph conducts all operations in the TensorFlow, which a set of computations that takes place successively. Op node is the term for each operation conducted, and nodes are connected.
The graph is responsible for outlining the ops and connections between the nodes, but it does not display the values.
2. Graph
TensorFlow uses Graph framework. During the training, the graph gathers and describes all the series computation. The graph offers advantages like
Running multiple CPUs or GPUs and even mobile operating systems.
The portability feature allows us to preserve the computation for immediate or later use.
The computations in the graph are done by connecting tensor, with the help of a node and an edge. The node is responsible for carrying out the mathematical operation, and the node produces endpoints outputs while the edges explain the input/output relationships between nodes.
Pytorch vs Tensorflow: Head to Head Comparison
Also, in the case of PyTorch, the code requires frequent checks for CUDA availability.
PyTorch | TensorFlow | |
Developed by | ||
Graphs | Dynamic graphs | Static graphs |
Distinguish Feature | TensorBoard | Support for CUDA |
Learning curve | Easy to learn | Steep learning curve |
Community | Comparatively small | Large |
Deployment | Comparatively less supportive | Supportive |
Debugging | Dynamic computational process. | Requires the TensorFlow debugger tool. |
Projects | cheXNet, PYRO, Horizon | Magenta, Sonnet, Ludwig |
Conclusion
Both frameworks are useful and have a huge community behind them. Both provide machine learning libraries to achieve various tasks and get the job done. TensorFlow is a powerful and deep learning tool with active visualization and debugging capabilities. TensorFlow also offers serialization benefits as the entire graph is saved as a protocol buffer. It also has support for mobile platforms and offers production-ready deployment. PyTorch, on the flip side, is still gaining momentum and attracting Python developers as it’s more Python friendly. In summary, TensorFlow is used to make things faster and build AI-related products, while research-oriented developers prefer PyTorch.
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