We achieved an F1 score (harmonic mean of precision and recall) of 0.68, which is quite decent considering the limited size of our dataset. My role was related to hyperparameter optimization for the LRCN model, wherein I experimented with different values of the learning rate, dropout, and regularization techniques and how they impacted the results of our model. One important take from the entire experience was how teamwork is crucial to produce an efficient output. The internship was rigorous, with early morning lectures and late night team meetings, but I learned a lot and had fun in the process!
Describe the input and output of a classification model Prepare data with feature engineering techniques Tackle both binary and multiclass classification problems Implement Support Vector Machines, Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbors, Neural Networks, logistic regression models on Python Use a variety of performance metrics such as confusion matrix, accuracy, precision, recall, ROC curve and AUC score. Use a variety of performance metrics such as confusion matrix, accuracy, precision, recall, ROC curve and AUC score. Artificial intelligence and machine learning are touching our everyday lives in more-and-more ways. There's an endless supply of industries and applications that machine learning can make more efficient and intelligent. Supervised machine learning is the underlying method behind a large part of this.
Centuries are a celebrated event in cricket, usually resulting in match-winning innings by the batsman. As a statistics enthusiast, it felt like a great problem to model because it is not only immensely interesting, the novelty of the problem did make it challenging. This piece explains the reasoning behind how I prepared the data, what model I used, and the evaluation criteria. In a previous post, I did a probabilistic analysis of centuries, a key finding was that unconditioned on anything else, the empirically estimated probability of a batsman knock resulting in a century is only 3.16%. This is important because when modeling a classification problem, class prevalence is probably the most crucial factor in determining the efficacy of your model(s).
Cancer is a disease that stems from the disruption of cellular state. Through genetic perturbations, tumor cells attain cellular states that give them proliferative advantage over the surrounding normal tissue . The inherent variability of this process has hampered efforts to find highly effective common therapies, thereby ushering the need for precision medicine . The scale of single-cell experiments is poised to revolutionize personalized medicine by effective characterization of the complete heterogeneity within a tumor for each individual patient [3, 4]. Recent expansion of single-cell sequencing technologies has exponentially increased the scale of knowledge attainable through a single biological experiment .
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The Patent Act requires an "inventor" to be a natural person, the US Court of Appeals for the Federal Circuit said, rejecting computer scientist Stephen Thaler's bid for patents on two inventions he said his DABUS system created. Thaler said in an email Friday that DABUS, which stands for "Device for the Autonomous Bootstrapping of Unified Sentience," is "natural and sentient." His attorney Ryan Abbott of Brown Neri Smith & Khan said the decision "ignores the purpose of the Patent Act" and has "real negative social consequences." He said they plan to appeal. The US Patent and Trademark Office declined to comment on the decision.
Thaler had asked for patents on behalf of his AI system Court affirms ruling that patent'inventor' must be human being Court affirms ruling that patent'inventor' must be human being The Patent Act requires an "inventor" to be a natural person, the U.S. Court of Appeals for the Federal Circuit said, rejecting computer scientist Stephen Thaler's bid for patents on two inventions he said his DABUS system created. Thaler said in an email Friday that DABUS, which stands for "Device for the Autonomous Bootstrapping of Unified Sentience," is "natural and sentient." His attorney Ryan Abbott of Brown Neri Smith & Khan said the decision "ignores the purpose of the Patent Act" and has "real negative social consequences." He said they plan to appeal. The U.S. Patent and Trademark Office declined to comment on the decision.
For decades, scientists have been giddy and citizens have been fearful of the power of computers. In 1965 Herbert Simon, a Nobel laureate in economics and also a winner of the Turing Award (considered "The Nobel Prize of computing"), predicted that "machines will be capable, within 20 years, of doing any work a man can do." His misplaced faith in computers is hardly unique. Sixty-seven years later, we are still waiting for computers to become our slaves and masters. Businesses have spent hundreds of billions of dollars on AI moonshots that have crashed and burned. Watson" was supposed to revolutionize health care and "eradicate cancer."
What happens when cyber criminals face robots? What happens when they use robots? How will offensive and defensive strategies of cybersecurity evolve as artificial intelligence continues to grow? Both artificial intelligence and cybersecurity have consistently landed in the top charts of fastest growing industries year after year¹². The 2 fields overlap in many areas and will undoubtedly continue to do so for years to come. For this article, I have narrowed my scope to a specific use case, intrusion detection. An Intrusion Detection System (IDS) is software that monitors a company's network for malicious activity. I dive into AI's role in Intrusion Detection Systems, code my own IDS using machine learning, and further demonstrate how it can be used to assist threat hunters.
In order to address a specific problem, practitioners must select an acceptable learning algorithm. A general rule of thumb is that for classification issues, we should use algorithms with high accuracy, whereas for regression problems, we should choose algorithms with lower accuracy but higher robustness because the absolute error rate is unimportant. Here are a few examples: Linear Regression: Linear regression uses the linearity principle to predict continuous values from a set of input variables. It achieves this by minimizing the total of squared errors. This method is fast and scalable for huge data sets since it avoids iterating over all possible replies; nonetheless, it is unstable.